Research ArticleRecognition and Parameter Extraction of One-DimensionalElectronic Scanning for 3D Radar
Cheng Li12 Wei Wang12 Longfei Shi12 and Xuesong Wang23
1College of Electronic Science and Engineering National University of Defense Technology Changsha 410073 China2State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronics and Information SystemNational University of Defense Technology Changsha 410073 China3College of Science National University of Defense Technology Changsha 410073 China
Correspondence should be addressed to Cheng Li lchnudtgmailcom
Received 15 May 2014 Revised 5 August 2014 Accepted 6 August 2014 Published 25 December 2014
Academic Editor Matteo Pastorino
Copyright copy 2014 Cheng Li et alThis is an open access article distributed under the Creative CommonsAttribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
Most of the existing antenna scan type (AST) identification techniques focus on onlymechanical scanning (MS) and rarely researchelectronic scanning (ES) especially one-dimensional (1D) ESThis paper proposes a recognition and parameter extraction methodof 1D ES for 3D radar which employs both MS and ES An ES pattern simulator is designed to synthesize pulse amplitude (PA)data first Then 1D ES is distinguished from MS and two-dimensional (2D) ES based on the features extracted from the differencesequences of beam sequence such as resemblance coefficient (RC) and variance Subsequently two vital parameters for 1D ES areextracted namely beam position number in elevation and pulse number in a beam position Finally some simulation results areshown to validate the proposed algorithm
1 Introduction
In modern electronic warfare (EW) 3D radar [1ndash3] withelectronically scanned beam in elevation namely one-dimensional (1D) electronic scanning (ES) has been widelyused for tactical applicationsWith the character of inertialessquick beam steering active electronically scanned array [4]allows 3D radar to provide elevation information with rangeand azimuth simultaneously Meanwhile 3D radar brings anew set of challenges for electronic countermeasure (ECM)It is inadequate for electronic intelligence (ELINT) [5] andelectronic support measurement (ESM) systems to obtainradar information based on the conventional parameters [6]such as radio frequency (RF) angle of arrival (AOA) timeof arrival (TOA) pulse width (PW) pulse amplitude (PA)and pulse repetition interval (PRI) On the other hand itis notoriously difficult to jam 3D radar by track deception[7] Therefore it is necessary to analyze some distinctivecharacters of 3D radar for example 1D ES
The characteristic parameters of 1DES including antennascan type (AST) antenna scan period (ASP) and someassociated scanning parameters are vital for EW systems
From those parameters we can infer the antenna scanningtechnique recognize the radar type and even determinethe threaten level Despite the importance there is a lackof studies in the open literature on 1D ES recognition andparameter extraction The only related works mainly focuson the AST of mechanical scanning (MS) radar In patents[8ndash10] various antenna scan pattern generators for MS radarsimulation are invented Literature [11] proposes a novelradar signal model which is able to generate radar signalsfor various scan patterns In patent [12] Greer presents anautomatic recognition method based on Laplace transformand fast Fourier transform (FFT) for basic ASTs Howeverthe variation of ASP and other parameters is neglected in thealgorithm In literature [13] Barshan and Eravci provide anovel ASP estimation and AST classification technique basedon pattern recognition Their algorithm seems effective androbust for 5 basic ASTs circular scan sector scan raster scanhelical scan and conical scan However their studies do nottake into account the ES
The most obvious difference between MS and ES isthat the latter changes the beam direction inertialessly and
Hindawi Publishing CorporationInternational Journal of Antennas and PropagationVolume 2014 Article ID 567954 9 pageshttpdxdoiorg1011552014567954
2 International Journal of Antennas and Propagation
quickly Since 3D radar employs both MS and ES it ismore complex to analyze its characters To the best ofour knowledge the research on 1D ES identification andparameter extraction has remained elusive The main con-tribution of this paper is providing lead recognition andparameter extraction of 1D ES for 3D radar Firstly weintroduce the resemblance coefficient (RC) of the first- andsecond-order difference sequences as an efficient feature todistinguish ES from MS Secondly we give an approachto extract key parameters of 1D ES for 3D radar includ-ing beam position number in elevation and pulse num-ber in a beam position Furthermore we also make someimprovements in the paper such as the beam sequenceextraction procedure Many ELINT and ESM systems maybenefit from our recognition of 1D ES Furthermore theparameters extracted in this paper including ASP beamposition number in elevation and pulse number in a beamposition are crucial for estimating the beam direction of 3Dradar
The rest of this paper is organized as follows Section 2provides some fundamental models and briefly describes theES pattern simulator to synthesize PA data In Section 3the methodology for recognition and parameter extractionof 1D ES is explained in detail Some simulation results arepresented in Section 4 Finally the conclusion follows
2 Model and Simulator
EW receiver detects radar parameters to acquire radar infor-mation such as RF AOA TOA PW and PA For antennascanning analysis time-domain analysis techniques based onmeasured PA data are commonly introducedThere is lack ofrecorded EW receiver data because of the classified nature ofEW work Therefore it is necessary to simulate the receiveddata based on signal models especially PA data
PA is the received pulse signal power of the EW antennaand is given by
119875119903
(119905) =1198751199051198661199031198661199051205822
(4120587119877)2
119871119865119905
[120579 (119905) 120601 (119905)] (1)
where 119875119903and 119875
119905are the received and the transmitted power
respectively 119866119903and 119866
119905are the antenna gains of EW receiver
and transmitter respectively 120582 is the wavelength 119877 is thedistance between the radar and the EW receiver 119871 is thepropagation loss factor and 119865
119905[120579(119905) 120601(119905)] is the transmitter
antenna pattern at the offset angels [120579(119905) 120601(119905)] where thereceiver is located at time 119905 and 120579(119905) and 120601(119905) are the azimuthangle and the elevation angle respectively
For the sake of simplicity we assume that the radar andthe EW receiver both remain stationary the receiver antennais isotropic and the propagation loss is constant As a resultthe term 119875
1199051198661199031198661199051205822
(4120587119877)2
119871 is assumed to be constant andthe PA is mainly determined by 119865
119905[120579(119905) 120601(119905)]
Figure 1 An example screen of the ES pattern simulator
The transmitter antenna of the 3D radar is modeled as aplanar array antenna in this paper For a planar array antennawith 119872 times 119873 elements the antenna pattern is given by [14]
119865 (120579 120593)
=
119872minus1
sum
119898=0
119873minus1
sum
119899=0
119868119898119899
119890119895119896[119898119889
119909(sin 120579 cos120593minussin 120579
0cos1205930)+119899119889119910(sin 120579 sin120593minussin 120579
0sin1205930)]
(2)
where 119868119898119899
is the (119898 119899) element of the array weighting matrix119868119872times119873
119889119909and 119889
119910are the element spacing along 119909- and 119910-
directions respectively 1205790and 120601
0are the elevation angle and
azimuth angle of beam respectively and 119896 = 2120587120582In this paper an ES pattern simulator with a graphical
user interface (GUI) is developed using MATLAB [15] Anexample screen of the simulator is illustrated in Figure 1where the parameter setting part is located on the upper sideand 3 subgraphs are shown on the lower side including thebeam boresight trace the 2D contour of offset angle and thereceived PA versus TOA plot
By the simulator the ES antenna with optional weight-ings (uniform Hamming Hanning Taylor Kaiser andBlackman) and different scanning parameters (startendazimuthelevation angles azimuthelevation element num-ber azimuthelevation element distance beamposition num-ber in elevation and pulse number in a beam position etc)can be simulated
3 Methodology
The key step of the methodology in this paper is to recognize1D ES As mentioned above the measured PAs changecontinuously because of the inertial movement of beam forMS For ES the PAs of the same beam position are nearly thesame while those of different beam positions even adjacentbeampositionsmay be rather different Consequently ES canbe distinguished fromMS based on this difference
The flowchart of the proposed method in this paper isshown in Figure 2 Firstly some processing (normalizationand ASP estimation) is carried out for beam sequenceextraction Then 1D ES is recognized based on the features
International Journal of Antennas and Propagation 3
Beamsequenceextraction
Normalization ASPestimation
Featuresextraction
1D ESrecognition
Parameterextraction
PA versus TOAdata
Figure 2 The flowchart of the methodology
0 5 10 15 20 25 30
0
Time (s)
PA (d
Bm)
minus60
minus40
minus20
(a)
0 5 10 15 20 25 30Time (s)
0
05
1
Nor
mal
ized
PA
(b)
Figure 3 An example PA versus a TOA signal for 3D radar and its normalized sequence
extracted from the beam sequence by utilizing the RC [16]Finally two vital parameters for 1D ES are extracted namelybeam position number in elevation and pulse number in abeam position
31 Normalization Assume that the received or simulatedPA and TOA data are 119886[119898] and 119905[119898] respectively 119898 =
0 1 119872 minus 1 where 119872 is the pulse number The inputsignal should have at least two cycles of the antenna scan forASP estimation Figure 3(a) shows an example PA versus aTOA signal with three periods from a 3D radar of Hammingweighting type In order to eliminate the influence of propa-gation normalization is needed for PA data While PA dataare in dBW (decibel relative to Watt) scale the maximumvalue of which may be zero the data is transformed fromdBW to Volt (V) first and normalized subsequently Consider
119886119881
[119898] = 10119886[119898]20
119898 = 0 1 119872 minus 1
119886 [119898] =119886119881
[119898]
max (119886119881
) 119898 = 0 1 119872 minus 1
(3)
where max(sdot) stands for the maximum value of the sequencein round brackets In this paper a sequence can also bedenoted by a letter or letters in curly brackets for example119886119881
As a result of normalization the example sequence
in Figure 3(a) is transformed to the sequence shown inFigure 3(b)
32 ASP Estimation The AST analysis is based on PAsequence with only one period so we estimate the ASP firstTheASP estimation algorithmused in literature [13] is appliedin this paper
In modern EW various types of PRI modulation areemployed such as constant jittered staggered sliding wob-ulated and D amp S (Dwell and Switch) [5] It will makethe sampling of PA data nonuniform and result in complexsucceeding processing In order to estimate the ASP it isbetter to resample the received data uniformly
Resampling with the shortest pulse interval is a simplemeasure while it does not take into account the PRI mod-ulation For exactness we determine the sampling intervalafter the analysis of PRI modulation Different PRI analysismethods such as CDIF [17] SDIF [18] PRI transform [19]and plan transform [20] can be used here and differentresampling intervals are chosen for different PRImodulationFor example the signal with stagger-type PRI is resampledwith the highest PRI in the stagger levels [13] and thatwith jitter-type PRI is resampled with the average PRI Itis well known that PRI analysis is crucial to radar signalsorting Consequently resampling signals based on PRImod-ulation information can maintain as many original signalsas possible and reduce the influence of dropped and falsepulses Furthermore the result of PRI analysis can also beuseful in the next section During the resampling processinterpolation is performed if the signal value is not available ata particular instant Different interpolation techniques can beused including nearest point linear polynomial Gaussiansinc or spline interpolation For simplicity we choose nearestpoint interpolation here and the points where no pulses aredetected by receiver are filled with zeros
Suppose that the resampling interval chosen based onthe result of PRI analysis is 119879
119901(for the example signal in
Figure 3 119879119901is chosen to 0002 s) and the resampled sequence
is 119909[119899] 119899 = 0 1 119873 minus 1 where 119873 is the sequence lengthThe normalized autocorrelation coefficients of the
sequence 119909 are calculated to estimate the ASP
119903119909119909
[119897] =sum119882minus1
119899=0119909 [119899] 119909 [119899 + 119897]
radicsum119882minus1
119899=01199092 [119899]radicsum
119882minus1
119899=01199092 [119899 + 119897]
119897 = 0 1 119873 minus 119882
(4)
where 119897 is the lag variable and 119882 is the window length thatis chosen to compromise between computational complexityand accuracy Since the sequence 119909 is assumed to have atleast two periods 119882 is chosen as 1198732 in this paper
The first maximum value of the sequence 119903119909119909
[119897] is foundand the corresponding lag value 119897
119889is assigned as the period
4 International Journal of Antennas and Propagation
0 1000 2000 3000 4000 5000 6000 7000 80000
01
02
03
04
05
06
07
08
09
1
l (lag)
r xx
(l)
Figure 4 Normalized autocorrelation coefficients of examplesequence
Hence the ASP can be obtained by 119879119860
= 119897119889
sdot 119879119901 The normal-
ized autocorrelation coefficients of the example sequence areillustrated in Figure 4 from which we can obtain 119897
119889= 5000
so that 119879119860
= 10 s
33 Beam Sequence Extraction After ASP estimation thesignal of one scan circle can be extracted for followingprocessing During the extraction make sure the circles areintegrated and the main beam is located in the middle ofevery circle Assume that the sequence with one periodextracted from the normalized input signal before resamplingis 119886[119899
1] 119886[119899
2]
When the radar beam steers through the location of theEW receiver in a scanning circle the received PA reachesthe maximum in the period Therefore we can locate themaximum main beam by finding the maximum PA firstIn literature [13] the main beam sequence is extracted byusing the two index points on each side of the maximumpoint where the PA drops to 001mV However it does nottake into account the ES Unlike the analysis of MS whichis based on only main beams that of ES is based on beamgroup including both main beam and adjacent side beamsConsequently the key of beam sequence extraction is to findthe edge of side beams
Because of the quick switch of beam direction to ES thechange of received PA data may be drastic The PA may dropto 001mV or other PA threshold values even during themainbeam scanning hence the method of beam extraction basedon PA threshold is no longer valid In this study we extractthe beam sequence based on received TOA data
The PAs of side beams are commonly much less thanthose of main beam When the PA is below the sensitivitylevel of the EW receiver the pulse will not be interceptedMost signals of side beams except the first side beam maybe undetected by EW receiver As a result there will be someintervals between detected pulses much longer than ordinaryPRIs
In order to determine the edges of side beams we seta PRI threshold 119867
119905according to the PRI analysis result in
Section 32 and then find twonearest PRIs that are longer than119867119905on both sides of the main beam The concrete procedures
of beam sequence extraction are given below
Step 1 Find the maximum PA 119886[119872119886] 1198991
lt 119872119886
lt 1198992 This
step is used to locate the main beam in the scan circleStep 2 Initialization Set bit = 1 119894 = 0Step 3 Compute the PRI on the right side of the main beam
one by one Let 119894 = 119894 + 1 119898 = 119872119886
minus 119894 times bit compute1199051015840
= 119905[119898 + 1] minus 119905[119898]Step 4 If 119905
1015840
lt 119867119905 119867119905being the threshold of pulse interval
repeat Step 3 otherwise if 1199051015840
ge 119867119905 the edge of right
side beam can be determined denote current 119898 by1198981
Step 5 Initialization Set bit = minus1 119894 = 0Step 6 Compute the PRI on the left side of the main beam
one by one Let 119894 = 119894 + 1 119898 = 119872119886
minus 119894 times bit compute1199051015840
= 119905[119898] minus 119905[119898 minus 1]Step 7 If 119905
1015840
lt 119867119905 repeat Step 6 otherwise if 119905
1015840
ge 119867119905 the edge
of left side beam can be determined denote current119898 by 119898
2
Step 8 Extract the beam sequence 119886119898
= 119886[1198981] 119886[119898
1+
1] 119886[1198982
minus 1] 119886[1198982] and the corresponding TOA
sequence 119905[1198981] 119905[1198981
+ 1] 119905[1198982
minus 1] 119905[1198982]
Besides the first side beams some other side beams mayalso be extracted after these steps It will not affect thefollowing recognitionThe beam sequence extracted from theexample sequence in Figure 3 is depicted in Figure 5(a) andFigure 5(b) shows a typical beam sequence of MS extractedby the above procedure
34 Feature Extraction According to the previous analysisthe crucial difference of PA sequence between MS and ESlies at the points where beams switch Since the antennabeam moves inertially for MS the measured PAs changecontinuously For ES the PAs of the same beamposition showslight variation while when beam direction changes theremay be a dramatic change of PAs Therefore it is necessaryto analyze the variations of PA sequence and the processingthat first occurs is calculating the difference sequence
At first the first-order difference sequence of the PA datais calculated
1198891
[119898] = 119886119898
[119898 + 1] minus 119886119898
[119898] 119898 = 0 1 119873119889 (5)
where 119873119889
= 1198982
minus 1198981 The first-order difference sequences
of example sequences in Figure 5 are illustrated in Figure 6It is easy to see that the sequence 119889
1 reflects the amplitude
variation of adjacent pulses For ES the values of most pointsin sequence 119889
1 are close to 0 while at the points where
main or side beams switch the values reach local maxima orminima The maximum occurs at the point where the mainbeam changes to the first side beam and its value is slightlyless than 1 Similarly for MS there are also local maxima orminima where main or side beams switch and the maximumalso occurs at the switch point between the main beam and
International Journal of Antennas and Propagation 5
64 65 66 67 68 69 7 71 72 730
05
1
Time (s)
Nor
mal
ized
PA
(a) ES
0
05
1
Nor
mal
ized
PA
64 65 66 67 68 69 7 71
Time (s)
(b) MS
Figure 5 Beam sequence
0 50 100 150 200 250 300 350 400minus1
minus05
0
05
1
Index
1st
diffe
renc
e
120 140 160 180minus1
0
1
(a) ES
0 50 100 150 200 250 300minus01
0
01
02
03
Index
1st
diffe
renc
e(b) MS
Figure 6 First-order difference sequence
0 50 100 150 200 250 300 350 400
005
1
Index
minus05minus12
nd d
iffer
ence
(a) ES
0 50 100 150 200 250 300
0005
01015
Index
minus0052nd
diff
eren
ce
(b) MS
Figure 7 Second-order difference sequence
side beam What is different is that the sequence 1198891 shows
gradual change at most points for MS and the maximumvalue is much less than 1
Because most values of sequence 1198891 for ES are near 0
we further calculate the second-order difference sequence
1198892
[119898] = 1198891
[119898 + 1] minus 1198891
[119898] 119898 = 0 1 119873119889
minus 1 (6)
Then theremay be slight variation between 1198892[119898] and 119889
1[119898+
1] for most 119898 for ES The second-order difference sequencesof example sequences in Figure 5 are illustrated in Figure 7
In order to accomplish the recognition we haveattempted to study a number of different features to identifyES but the best results were obtained with the featuredescribed below
By comparing the first-order difference sequences inFigure 6 with the second-order ones in Figure 7 we foundthat there are some notable differences between ES and MSFor ES the first- and second-order difference sequences seemsimilar at most points except the points where main or sidebeams change while for MS the two sequences are quite
differentTherefore we select RC as the feature to distinguishMS and ES
Definition 1 Suppose that discrete signal sequences 1198781(119894) 119894 =
0 1 119873 and 1198782(119895) 119895 = 0 1 119873 are one-dimensional
and nonnegative that is 1198781(119894) ge 0 119878
2(119894) ge 0 119894 = 0 1 119873
RC of 1198781 and 119878
2 is defined as
119862119903
=sum 1198781
(119894) 1198782
(119894)
(radicsum 1198782
1(119894) sdot radicsum 119878
2
2(119894))
(7)
where all points of signal sequences 1198781(119894) and 119878
2(119895) are not
0 Obviously we can get 0 le 119862119903
le 1In order to obtain the RC from the two difference
sequences we set 1198782(119894) = |119889
2(119894)| 119894 = 0 1 119873
119889minus 1 and
1198781 is given by the following formula
1198781
(119894) = max (10038161003816100381610038161198891 (119894 + 1)
1003816100381610038161003816 10038161003816100381610038161198892 (119894)
1003816100381610038161003816) 119894 = 0 1 119873119889
minus 1
(8)
Then the RC can be calculated using formula (7) The valueof 119862119903is large for ES and nearly equal to 1 while it is much less
for MS
6 International Journal of Antennas and Propagation
Features
Cr gt Hr Cr Hr
ES MS
Vd gt H Vd H
1D ES 2D ES
⩽
⩽
Figure 8 The DT structure for recognition of 1D ES
Furthermore in order to differentiate 1D ES from 2D ESwe further process the first-order difference sequence 119889
1
First pick up the elements less than 119867119886in sequence |119889
1|
119867119886is the threshold Hence the picked points present the
variations among a beam position for ES Then compute thevariance of picked elements which is denoted by 119881
119889 Because
there is no beam movement among a beam position for 2DES 119881119889of 2D ES is commonly less than that for 1D ES for
which beam still moves inertially in azimuth
35 Recognition The recognition of 1D ES has two stagesfirst distinguishing ES from MS and then distinguishing 1DES from 2D ES
After extracting the two features we can accomplishthe recognition by utilizing a classifier such as naive Bayesdecision tree (DT) [21] clustering analysis artificial neuralnetworks [22] and support vectormachine [22] Based on therecognition procedure in this paper a DT classifier based onclustering analysis is chosen to recognize 1D ES
A DT classifier is one of the most intuitive and naturalclassifiers and it is fast comprehensible and easy to visualizeA DT consists of two kinds of nodes an internal noderepresents a decision rule and a leaf node shows the resultof a decisionThe DT structure used for recognition of 1D ESis shown in Figure 8
Clustering analysis is the task of grouping a set of objectsin such a way that objects in the same cluster are moresimilar to each other than to those in other clusters It isused in many fields including pattern recognition machinelearning and information retrieval In this paper clusteringis mainly used to determine the threshold of decision rulein DT The Euclidean distance criterion is most commonlyemployed in clustering and the Euclidean distance between1199111
= (11991111
1199111119895
) and 1199112
= (11991121
1199112119895
) of instances isdefined as
119889 (1199111 1199112) = radic
119895
sum
119894=1
(1199111119894
minus 1199112119894
)2
(9)
Since there is only one feature applied in each decisionrule the cluster center can be easily obtained by takingthe mean of train data with every AST For simplicity we
EW receiver
Radar
Beam position variation in
elevation
Azimuth
rotation
NB
NP A
Figure 9 A sketch map of NB and NP for 3D radar with 1D ES
select the median of the cluster centers of two leaf nodesas the threshold in each decision Then 119867
119903and 119867V can be
determined and recognition can be carried out according tothe DT in Figure 8
36 Parameter Extraction This paper mainly considers the1D ES of 3D radar which employs a judicious mix of elec-tronic beam steering orderly in elevation and mechanicallymovement in azimuth After identification of the 1D ES fromMS and 2D ES we need to extract some crucial parametersof 1D ES of 3D radar Besides ASP the key parameters of1D electronic scanning are the beam position number inelevation (defined by NB) and pulse number in a beamposition (defined by NP) which are important to estimatethe beam direction of 3D radar as shown in Figure 9 Forsimplicity bothNB andNP are assumed to be constant in thisstudy The NB and NP are 6 and 4 respectively in Figure 9
The parameter extraction of 1D ES of 3D radar is mainlybased on the beam sequence 119886
119898 extracted in Section 33
From the first-order difference sequence of the sequence119886119898
shown in the subgraph of Figure 6(a) we can see thatthere exist a peak and a trough at two sides of every beamposition which result from the beam position variation inelevation Figure 10 depicts the first difference sequence of thesubgraph and its corresponding PA sequence Consequentlywe can extract NP by determining the interval between thehighest peak and the lowest trough of the beam position withmaximum PA Firstly locate the maximum PA of sequence119886119898
denoted by 119886119898
[119872119887] 0 lt 119872
119886lt 119873119889 Obviously 119886
119898[119872119887]
is equal to 119886[119872119886] which is extracted in Section 33 Then as
for the sequence 1198891 locate the nearby highest peak before
1198891[119872119887] (denoted by 119889
1[119872119901
]) and the nearby lowest troughafter 119889
1[119872119887] (denoted by 119889
1[119872119905]) For the example sequence
shown in Figure 5(b) both 1198891[119872119901
] and 1198891[119872119905] are marked
with red circles in Figure 10(b) Hence the pulse number in abeam position can be calculated by 119873119875 = 119872
119905minus 119872119901
In order to extract NB the steer circle in elevation of1D ES that is the pulse number in an elevation circleshould be determined An example of an elevation circlesequence is marked with red in Figure 10(a) Because thebeam sequences of two adjacent elevation circles are similarin amplitude we can compare the entire sequence 119886
119898 with
reference sequence to estimate the elevation circle intervaland normalized cross-correlation coefficients are applied
International Journal of Antennas and Propagation 7
66 662 664 666 668 670
05
1
Time (s)
Nor
mal
ized
PA
Elevation circlesequenceBeam position
sequence
Referencesequence
(a) Normalized PA
120 130 140 150 160 170 180minus1
minus05
0
05
1
Index
1st
diffe
renc
e
Mt
Mp
(b) 1st difference
Figure 10 An extracted PA sequence and first difference sequence for 1D ES
here Firstly we determine the reference sequence After NPextraction the beam position sequence with maximum PAcan be obtained easily namely 119886
119898[119872119901
+ 1] 119886119898
[119872119905]
marked with blue in Figure 10(a) Then we select the PA dataof the maximum beam position and the adjacent beam posi-tions at its both sides as the reference sequence 119910[119898] 119898 =
0 1 119873119910
minus 1 marked with black in Figure 10(a) Hence119910 = 119886
119898[119872119901
+1minus119873119875] 119886119898
[119872119905+119873119875] and119873
119910= 3times119873119875
Calculate the mean square errors (MSE) of sequence 119910 and119886119898
119890119886119910
[119897] = radic
119873119910minus1
sum
119899=0
(119910 [119899] minus 119886119898
[119899 + 119897])2
119897 = 0 1 119873119889
+ 1 minus 119873119910
(10)
The MSE sequence of the example sequence is shownin Figure 11 from which we can see that in sequence 119890
119886119910
there are several troughs which are located where there aremain beam positions Among these troughs the lowest onewhose value is equal to 0 represents themaximummain beamposition namely the reference sequence 119910 Furthermorethe secondary lowest trough represents the adjacent mainbeam position Let 119873
119898denote the interval of the two troughs
which are marked with red circles in Figure 11 so that NB canbe obtained by 119873119861 = 119873
119898119873119875
4 Simulations
In order to illustrate the proposed algorithm for recognitionof 1D ES several simulations are discussed in this section
41 Recognition At first 60 data sequences are generatedfor training of feature extraction and recognition describedin Sections 34 and 35 20 from 1D ES 20 from 2D ESand the remaining from MS All the ES data are generatedby the simulator designed in Section 2 and the MS data bythe antenna scan pattern simulator in literature [23] Whilegenerating the simulated data we have varied some differentparameters (eg periodweightings angles and so on) so thatthe simulated data resemble the actual data from differentrealistic scenarios as much as possible
0 50 100 150 200 250 300 350 4000
05
1
15
2
25
3
Index
MSE
Figure 11 The MSE sequence
Table 1 Recognition results
Input Classification Recognition rate ()1D ES 2D ES MS
1D ES 88 11 1 882D ES 8 92 0 92MS 4 1 95 95
The features extracted from the simulated data sequenceare depicted in Figure 12 We can see that the feature 119862
119903is
distinguishable Most 119862119903values are equal to 1 for both 1D ES
and 2D ES Although the values of 119862119903are above 07 for MS
they are still less than those for ES The threshold 119867119903is set to
088 based on the train data Moreover the 119881119889value for 1D ES
is generally greater than that for 2D ES and the threshold 119867Vis set to 000031
The number of simulated samples is increased to 100for each AST for recognition Table 1 presents the specificrecognition results The data illustrate that the methodproposed in this paper is effective to distinguish ES fromMS The recognition rates of different ASTs are equal to orabove 88 and the classification accuracy for ES (1D and 2D)even reaches 995 It is suggested to refer to literature [13]for further classification of specific MS type such as circularscan sector scan raster scan and helical scan
8 International Journal of Antennas and Propagation
2 4 6 8 10 12 14 16 18 2006
065
07
075
08
085
09
095
1
105
11
Data index
Cr
1D ES2D ESMS
(a) 119862119903
2 4 6 8 10 12 14 16 18 200
02
04
06
08
1
12
14
16
18
2times10minus3
Data index
Vd
1D ES2D ES
(b) 119881119889
Figure 12 The extracted features
Table 2 Simulation scenarios
Scenario MSlowast Scope Inlowastlowast NP extraction rate () NB extraction rate ()1 Circular Yes 95 952 Sector Yes 94 943 Circular No 94 934 Sector No 89 89lowast
Themechanical scanning type applied by 3D radar in azimuthlowastlowastDoes the EW receiver locate in the elevation scope of 3D radar
42 Parameter Extraction In order to validate the parameterextraction for 1D ES 4 different scenarios are considered inthis simulation which are listed in Table 2 When the EWreceiver does not locate in the elevation scope of 3D radar itcan only receive the signals from the side beams of 3D radarThe simulated samples in which the acquired pulses are notsufficient for parameter extraction are aborted
Unlike estimated parameters (eg ASP) which are avail-able in a tolerance range the main parameters extracted inthis paper (NP and NB) will be invalid if the extracted valuesare incorrect Therefore the extraction accuracy is used toevaluate the performance of parameter extraction here Theextraction result is illustrated in Table 2 It can be observedthat the correct extraction rate of NP is almost equal to thatof NB The reason is that the extraction of NB is based onthe result ofNP extractionThe extraction accuracies are over89 and with slight difference for all 4 scenarios It can bededuced that the parameter extraction is effective even by theside beams signals
5 Conclusions
In this paper we studied the problem of recognition andparameter extraction of 1D ES for 3D radar on which noclear and accessible study is available in the open literatureThe main contribution of this work is making a maidenattempt to resolve the problem and providing an effective
method Firstly an ES pattern simulator to synthesize PAdata is designed Then the methodology for recognition andparameter extraction of 1D ES for 3D radar is explained indetail Finally the algorithm is validated by simulation
Our study will be beneficial to 3D radar identificationfor ELINT and ESM systems Moreover it is also usefulfor estimating the beam direction of 3D radar Our mainfollowing work is to validate the algorithm by real radardata acquired by EW receivers To achieve the purpose someexperiments are going to be designed in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by the National Natural ScienceFoundation of China (no 61201336)
References
[1] J Wisniewski and M Mazur ldquoNaval 3D radar in C bandrdquoin Proceedings of the 14th IEEE International Conference onMicrowaves Radar andWireless Communications (MIKON rsquo02)vol 3 pp 843ndash846 2002
International Journal of Antennas and Propagation 9
[2] F Orsini B di Loreto and A Giacomini ldquo3D X-band tacticalacquisition radar (X-TAR 3D)rdquo in Proceedings of the EuropeanRadar Conference pp 589ndash592 October 2009
[3] D L Adamy EW 102 A Second Course in Electronic WarfareArtech House Boston Mass USA 2004
[4] A Irci A Saranli and B Baykal ldquoStudy on Q-RAM andfeasible directions based methods for resource management inphased array radar systemsrdquo IEEE Transactions on Aerospaceand Electronic Systems vol 46 no 4 pp 1848ndash1864 2010
[5] R G Wiley ELINT The Interception and Analysis of RadarSignals Artech House Boston Mass USA 2006
[6] M Zhu K-C Fu X-Y Huang S-D Wu and W-D Jin ldquoAnovel recognition approach for radar emitter signals based onon-line independent support vector machinesrdquo Advances inComputer Science and Its Applications vol 2 no 3 pp 390ndash3962013
[7] D H A Maithripala and S Jayasuriya ldquoRadar deceptionthrough phantom track generationrdquo in Proceedings of theAmerican Control Conference (ACC rsquo05) pp 4102ndash4106 June2005
[8] B Falk ldquoAntenna PatternGeneratorrdquoUS Patent 3540046 1970[9] A B Evan Jr ldquoDigital antenna pattern generator for radar
simulationrdquo US Patent 4008476 1977[10] T E Zaczek ldquoAntenna scan pattern generatorrdquo US Patent
4327417 1982[11] Y-H Kim W-J Kim K-H Song J-W Han and H-N Kim
ldquoModeling of a radar signal for scan patternrdquo in Proceedings ofthe IEEE Military Communications Conference (MILCOM rsquo09)pp 1ndash6 Vancouver Canada October 2009
[12] T H Greer ldquoAutomatic recognition of radar scan typerdquo USPatent 6697007 2004
[13] B Barshan and B Eravci ldquoAutomatic radar antenna scantype recognition in electronic warfarerdquo IEEE Transactions onAerospace and Electronic Systems vol 48 no 4 pp 2908ndash29312012
[14] X-S Wang S-P Xiao D-J Feng and F Zhao Modeling andSimulation of Modern Radar and Electronic Warfare SystemsPublishing House of Electronics Industry Xian China 2010
[15] B R Mahafza and A Z Elsherbeni MATLAB Simulations forRadar Systems Design CRC Press Boca Raton Fla USA 2004
[16] G-X Zhang and X Li ldquoA new recognition system for radaremitter signalsrdquo Kybernetes vol 41 no 9 pp 1351ndash1360 2012
[17] H K Mardia ldquoNew techniques for the deinterleaving ofrepetitive sequencesrdquo IEE proceedings vol 136 no 4 pp 149ndash154 1989
[18] D J Milojevic and B M Popovic ldquoImproved algorithm for thedeinterleaving of radar pulsesrdquo IEE Proceedings F vol 139 no 1pp 98ndash104 1992
[19] D J Nelson ldquoSpecial purpose correlation functions forimproved signal detection and parameter estimationrdquo in Pro-ceedings of the International Conference on Acoustics Speechand Signal Processing (ICASSP rsquo93) pp 73ndash76 1993
[20] P Wei L Qiu and H Wu ldquoThe sorting of radar pulsesignal based on plane transformationrdquo in Proceedings of theInternational Conference on Systems and Informatics (ICSAI rsquo12)pp 326ndash328 May 2012
[21] W-T Lo Y-S Chang R-K Sheu C-C Chiu and S-M YuanldquoCUDT a CUDA based decision tree algorithmrdquoThe ScientificWorld Journal vol 2014 Article ID 745640 12 pages 2014
[22] Q-D Wu B Yan C Zhang L Wang G-B Ning and BYu ldquoDisplacement prediction of tunnel surrounding rock acomparison of support vector machine and artificial neuralnetworkrdquo Mathematical Problems in Engineering vol 2014Article ID 351496 6 pages 2014
[23] B Eravcg Automatic radar antenna scan analysis in electronicwarfare [MS thesis] Department of Electrical and ElectronicsEngineering Bilkent University Ankara Turkey 2010
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
2 International Journal of Antennas and Propagation
quickly Since 3D radar employs both MS and ES it ismore complex to analyze its characters To the best ofour knowledge the research on 1D ES identification andparameter extraction has remained elusive The main con-tribution of this paper is providing lead recognition andparameter extraction of 1D ES for 3D radar Firstly weintroduce the resemblance coefficient (RC) of the first- andsecond-order difference sequences as an efficient feature todistinguish ES from MS Secondly we give an approachto extract key parameters of 1D ES for 3D radar includ-ing beam position number in elevation and pulse num-ber in a beam position Furthermore we also make someimprovements in the paper such as the beam sequenceextraction procedure Many ELINT and ESM systems maybenefit from our recognition of 1D ES Furthermore theparameters extracted in this paper including ASP beamposition number in elevation and pulse number in a beamposition are crucial for estimating the beam direction of 3Dradar
The rest of this paper is organized as follows Section 2provides some fundamental models and briefly describes theES pattern simulator to synthesize PA data In Section 3the methodology for recognition and parameter extractionof 1D ES is explained in detail Some simulation results arepresented in Section 4 Finally the conclusion follows
2 Model and Simulator
EW receiver detects radar parameters to acquire radar infor-mation such as RF AOA TOA PW and PA For antennascanning analysis time-domain analysis techniques based onmeasured PA data are commonly introducedThere is lack ofrecorded EW receiver data because of the classified nature ofEW work Therefore it is necessary to simulate the receiveddata based on signal models especially PA data
PA is the received pulse signal power of the EW antennaand is given by
119875119903
(119905) =1198751199051198661199031198661199051205822
(4120587119877)2
119871119865119905
[120579 (119905) 120601 (119905)] (1)
where 119875119903and 119875
119905are the received and the transmitted power
respectively 119866119903and 119866
119905are the antenna gains of EW receiver
and transmitter respectively 120582 is the wavelength 119877 is thedistance between the radar and the EW receiver 119871 is thepropagation loss factor and 119865
119905[120579(119905) 120601(119905)] is the transmitter
antenna pattern at the offset angels [120579(119905) 120601(119905)] where thereceiver is located at time 119905 and 120579(119905) and 120601(119905) are the azimuthangle and the elevation angle respectively
For the sake of simplicity we assume that the radar andthe EW receiver both remain stationary the receiver antennais isotropic and the propagation loss is constant As a resultthe term 119875
1199051198661199031198661199051205822
(4120587119877)2
119871 is assumed to be constant andthe PA is mainly determined by 119865
119905[120579(119905) 120601(119905)]
Figure 1 An example screen of the ES pattern simulator
The transmitter antenna of the 3D radar is modeled as aplanar array antenna in this paper For a planar array antennawith 119872 times 119873 elements the antenna pattern is given by [14]
119865 (120579 120593)
=
119872minus1
sum
119898=0
119873minus1
sum
119899=0
119868119898119899
119890119895119896[119898119889
119909(sin 120579 cos120593minussin 120579
0cos1205930)+119899119889119910(sin 120579 sin120593minussin 120579
0sin1205930)]
(2)
where 119868119898119899
is the (119898 119899) element of the array weighting matrix119868119872times119873
119889119909and 119889
119910are the element spacing along 119909- and 119910-
directions respectively 1205790and 120601
0are the elevation angle and
azimuth angle of beam respectively and 119896 = 2120587120582In this paper an ES pattern simulator with a graphical
user interface (GUI) is developed using MATLAB [15] Anexample screen of the simulator is illustrated in Figure 1where the parameter setting part is located on the upper sideand 3 subgraphs are shown on the lower side including thebeam boresight trace the 2D contour of offset angle and thereceived PA versus TOA plot
By the simulator the ES antenna with optional weight-ings (uniform Hamming Hanning Taylor Kaiser andBlackman) and different scanning parameters (startendazimuthelevation angles azimuthelevation element num-ber azimuthelevation element distance beamposition num-ber in elevation and pulse number in a beam position etc)can be simulated
3 Methodology
The key step of the methodology in this paper is to recognize1D ES As mentioned above the measured PAs changecontinuously because of the inertial movement of beam forMS For ES the PAs of the same beam position are nearly thesame while those of different beam positions even adjacentbeampositionsmay be rather different Consequently ES canbe distinguished fromMS based on this difference
The flowchart of the proposed method in this paper isshown in Figure 2 Firstly some processing (normalizationand ASP estimation) is carried out for beam sequenceextraction Then 1D ES is recognized based on the features
International Journal of Antennas and Propagation 3
Beamsequenceextraction
Normalization ASPestimation
Featuresextraction
1D ESrecognition
Parameterextraction
PA versus TOAdata
Figure 2 The flowchart of the methodology
0 5 10 15 20 25 30
0
Time (s)
PA (d
Bm)
minus60
minus40
minus20
(a)
0 5 10 15 20 25 30Time (s)
0
05
1
Nor
mal
ized
PA
(b)
Figure 3 An example PA versus a TOA signal for 3D radar and its normalized sequence
extracted from the beam sequence by utilizing the RC [16]Finally two vital parameters for 1D ES are extracted namelybeam position number in elevation and pulse number in abeam position
31 Normalization Assume that the received or simulatedPA and TOA data are 119886[119898] and 119905[119898] respectively 119898 =
0 1 119872 minus 1 where 119872 is the pulse number The inputsignal should have at least two cycles of the antenna scan forASP estimation Figure 3(a) shows an example PA versus aTOA signal with three periods from a 3D radar of Hammingweighting type In order to eliminate the influence of propa-gation normalization is needed for PA data While PA dataare in dBW (decibel relative to Watt) scale the maximumvalue of which may be zero the data is transformed fromdBW to Volt (V) first and normalized subsequently Consider
119886119881
[119898] = 10119886[119898]20
119898 = 0 1 119872 minus 1
119886 [119898] =119886119881
[119898]
max (119886119881
) 119898 = 0 1 119872 minus 1
(3)
where max(sdot) stands for the maximum value of the sequencein round brackets In this paper a sequence can also bedenoted by a letter or letters in curly brackets for example119886119881
As a result of normalization the example sequence
in Figure 3(a) is transformed to the sequence shown inFigure 3(b)
32 ASP Estimation The AST analysis is based on PAsequence with only one period so we estimate the ASP firstTheASP estimation algorithmused in literature [13] is appliedin this paper
In modern EW various types of PRI modulation areemployed such as constant jittered staggered sliding wob-ulated and D amp S (Dwell and Switch) [5] It will makethe sampling of PA data nonuniform and result in complexsucceeding processing In order to estimate the ASP it isbetter to resample the received data uniformly
Resampling with the shortest pulse interval is a simplemeasure while it does not take into account the PRI mod-ulation For exactness we determine the sampling intervalafter the analysis of PRI modulation Different PRI analysismethods such as CDIF [17] SDIF [18] PRI transform [19]and plan transform [20] can be used here and differentresampling intervals are chosen for different PRImodulationFor example the signal with stagger-type PRI is resampledwith the highest PRI in the stagger levels [13] and thatwith jitter-type PRI is resampled with the average PRI Itis well known that PRI analysis is crucial to radar signalsorting Consequently resampling signals based on PRImod-ulation information can maintain as many original signalsas possible and reduce the influence of dropped and falsepulses Furthermore the result of PRI analysis can also beuseful in the next section During the resampling processinterpolation is performed if the signal value is not available ata particular instant Different interpolation techniques can beused including nearest point linear polynomial Gaussiansinc or spline interpolation For simplicity we choose nearestpoint interpolation here and the points where no pulses aredetected by receiver are filled with zeros
Suppose that the resampling interval chosen based onthe result of PRI analysis is 119879
119901(for the example signal in
Figure 3 119879119901is chosen to 0002 s) and the resampled sequence
is 119909[119899] 119899 = 0 1 119873 minus 1 where 119873 is the sequence lengthThe normalized autocorrelation coefficients of the
sequence 119909 are calculated to estimate the ASP
119903119909119909
[119897] =sum119882minus1
119899=0119909 [119899] 119909 [119899 + 119897]
radicsum119882minus1
119899=01199092 [119899]radicsum
119882minus1
119899=01199092 [119899 + 119897]
119897 = 0 1 119873 minus 119882
(4)
where 119897 is the lag variable and 119882 is the window length thatis chosen to compromise between computational complexityand accuracy Since the sequence 119909 is assumed to have atleast two periods 119882 is chosen as 1198732 in this paper
The first maximum value of the sequence 119903119909119909
[119897] is foundand the corresponding lag value 119897
119889is assigned as the period
4 International Journal of Antennas and Propagation
0 1000 2000 3000 4000 5000 6000 7000 80000
01
02
03
04
05
06
07
08
09
1
l (lag)
r xx
(l)
Figure 4 Normalized autocorrelation coefficients of examplesequence
Hence the ASP can be obtained by 119879119860
= 119897119889
sdot 119879119901 The normal-
ized autocorrelation coefficients of the example sequence areillustrated in Figure 4 from which we can obtain 119897
119889= 5000
so that 119879119860
= 10 s
33 Beam Sequence Extraction After ASP estimation thesignal of one scan circle can be extracted for followingprocessing During the extraction make sure the circles areintegrated and the main beam is located in the middle ofevery circle Assume that the sequence with one periodextracted from the normalized input signal before resamplingis 119886[119899
1] 119886[119899
2]
When the radar beam steers through the location of theEW receiver in a scanning circle the received PA reachesthe maximum in the period Therefore we can locate themaximum main beam by finding the maximum PA firstIn literature [13] the main beam sequence is extracted byusing the two index points on each side of the maximumpoint where the PA drops to 001mV However it does nottake into account the ES Unlike the analysis of MS whichis based on only main beams that of ES is based on beamgroup including both main beam and adjacent side beamsConsequently the key of beam sequence extraction is to findthe edge of side beams
Because of the quick switch of beam direction to ES thechange of received PA data may be drastic The PA may dropto 001mV or other PA threshold values even during themainbeam scanning hence the method of beam extraction basedon PA threshold is no longer valid In this study we extractthe beam sequence based on received TOA data
The PAs of side beams are commonly much less thanthose of main beam When the PA is below the sensitivitylevel of the EW receiver the pulse will not be interceptedMost signals of side beams except the first side beam maybe undetected by EW receiver As a result there will be someintervals between detected pulses much longer than ordinaryPRIs
In order to determine the edges of side beams we seta PRI threshold 119867
119905according to the PRI analysis result in
Section 32 and then find twonearest PRIs that are longer than119867119905on both sides of the main beam The concrete procedures
of beam sequence extraction are given below
Step 1 Find the maximum PA 119886[119872119886] 1198991
lt 119872119886
lt 1198992 This
step is used to locate the main beam in the scan circleStep 2 Initialization Set bit = 1 119894 = 0Step 3 Compute the PRI on the right side of the main beam
one by one Let 119894 = 119894 + 1 119898 = 119872119886
minus 119894 times bit compute1199051015840
= 119905[119898 + 1] minus 119905[119898]Step 4 If 119905
1015840
lt 119867119905 119867119905being the threshold of pulse interval
repeat Step 3 otherwise if 1199051015840
ge 119867119905 the edge of right
side beam can be determined denote current 119898 by1198981
Step 5 Initialization Set bit = minus1 119894 = 0Step 6 Compute the PRI on the left side of the main beam
one by one Let 119894 = 119894 + 1 119898 = 119872119886
minus 119894 times bit compute1199051015840
= 119905[119898] minus 119905[119898 minus 1]Step 7 If 119905
1015840
lt 119867119905 repeat Step 6 otherwise if 119905
1015840
ge 119867119905 the edge
of left side beam can be determined denote current119898 by 119898
2
Step 8 Extract the beam sequence 119886119898
= 119886[1198981] 119886[119898
1+
1] 119886[1198982
minus 1] 119886[1198982] and the corresponding TOA
sequence 119905[1198981] 119905[1198981
+ 1] 119905[1198982
minus 1] 119905[1198982]
Besides the first side beams some other side beams mayalso be extracted after these steps It will not affect thefollowing recognitionThe beam sequence extracted from theexample sequence in Figure 3 is depicted in Figure 5(a) andFigure 5(b) shows a typical beam sequence of MS extractedby the above procedure
34 Feature Extraction According to the previous analysisthe crucial difference of PA sequence between MS and ESlies at the points where beams switch Since the antennabeam moves inertially for MS the measured PAs changecontinuously For ES the PAs of the same beamposition showslight variation while when beam direction changes theremay be a dramatic change of PAs Therefore it is necessaryto analyze the variations of PA sequence and the processingthat first occurs is calculating the difference sequence
At first the first-order difference sequence of the PA datais calculated
1198891
[119898] = 119886119898
[119898 + 1] minus 119886119898
[119898] 119898 = 0 1 119873119889 (5)
where 119873119889
= 1198982
minus 1198981 The first-order difference sequences
of example sequences in Figure 5 are illustrated in Figure 6It is easy to see that the sequence 119889
1 reflects the amplitude
variation of adjacent pulses For ES the values of most pointsin sequence 119889
1 are close to 0 while at the points where
main or side beams switch the values reach local maxima orminima The maximum occurs at the point where the mainbeam changes to the first side beam and its value is slightlyless than 1 Similarly for MS there are also local maxima orminima where main or side beams switch and the maximumalso occurs at the switch point between the main beam and
International Journal of Antennas and Propagation 5
64 65 66 67 68 69 7 71 72 730
05
1
Time (s)
Nor
mal
ized
PA
(a) ES
0
05
1
Nor
mal
ized
PA
64 65 66 67 68 69 7 71
Time (s)
(b) MS
Figure 5 Beam sequence
0 50 100 150 200 250 300 350 400minus1
minus05
0
05
1
Index
1st
diffe
renc
e
120 140 160 180minus1
0
1
(a) ES
0 50 100 150 200 250 300minus01
0
01
02
03
Index
1st
diffe
renc
e(b) MS
Figure 6 First-order difference sequence
0 50 100 150 200 250 300 350 400
005
1
Index
minus05minus12
nd d
iffer
ence
(a) ES
0 50 100 150 200 250 300
0005
01015
Index
minus0052nd
diff
eren
ce
(b) MS
Figure 7 Second-order difference sequence
side beam What is different is that the sequence 1198891 shows
gradual change at most points for MS and the maximumvalue is much less than 1
Because most values of sequence 1198891 for ES are near 0
we further calculate the second-order difference sequence
1198892
[119898] = 1198891
[119898 + 1] minus 1198891
[119898] 119898 = 0 1 119873119889
minus 1 (6)
Then theremay be slight variation between 1198892[119898] and 119889
1[119898+
1] for most 119898 for ES The second-order difference sequencesof example sequences in Figure 5 are illustrated in Figure 7
In order to accomplish the recognition we haveattempted to study a number of different features to identifyES but the best results were obtained with the featuredescribed below
By comparing the first-order difference sequences inFigure 6 with the second-order ones in Figure 7 we foundthat there are some notable differences between ES and MSFor ES the first- and second-order difference sequences seemsimilar at most points except the points where main or sidebeams change while for MS the two sequences are quite
differentTherefore we select RC as the feature to distinguishMS and ES
Definition 1 Suppose that discrete signal sequences 1198781(119894) 119894 =
0 1 119873 and 1198782(119895) 119895 = 0 1 119873 are one-dimensional
and nonnegative that is 1198781(119894) ge 0 119878
2(119894) ge 0 119894 = 0 1 119873
RC of 1198781 and 119878
2 is defined as
119862119903
=sum 1198781
(119894) 1198782
(119894)
(radicsum 1198782
1(119894) sdot radicsum 119878
2
2(119894))
(7)
where all points of signal sequences 1198781(119894) and 119878
2(119895) are not
0 Obviously we can get 0 le 119862119903
le 1In order to obtain the RC from the two difference
sequences we set 1198782(119894) = |119889
2(119894)| 119894 = 0 1 119873
119889minus 1 and
1198781 is given by the following formula
1198781
(119894) = max (10038161003816100381610038161198891 (119894 + 1)
1003816100381610038161003816 10038161003816100381610038161198892 (119894)
1003816100381610038161003816) 119894 = 0 1 119873119889
minus 1
(8)
Then the RC can be calculated using formula (7) The valueof 119862119903is large for ES and nearly equal to 1 while it is much less
for MS
6 International Journal of Antennas and Propagation
Features
Cr gt Hr Cr Hr
ES MS
Vd gt H Vd H
1D ES 2D ES
⩽
⩽
Figure 8 The DT structure for recognition of 1D ES
Furthermore in order to differentiate 1D ES from 2D ESwe further process the first-order difference sequence 119889
1
First pick up the elements less than 119867119886in sequence |119889
1|
119867119886is the threshold Hence the picked points present the
variations among a beam position for ES Then compute thevariance of picked elements which is denoted by 119881
119889 Because
there is no beam movement among a beam position for 2DES 119881119889of 2D ES is commonly less than that for 1D ES for
which beam still moves inertially in azimuth
35 Recognition The recognition of 1D ES has two stagesfirst distinguishing ES from MS and then distinguishing 1DES from 2D ES
After extracting the two features we can accomplishthe recognition by utilizing a classifier such as naive Bayesdecision tree (DT) [21] clustering analysis artificial neuralnetworks [22] and support vectormachine [22] Based on therecognition procedure in this paper a DT classifier based onclustering analysis is chosen to recognize 1D ES
A DT classifier is one of the most intuitive and naturalclassifiers and it is fast comprehensible and easy to visualizeA DT consists of two kinds of nodes an internal noderepresents a decision rule and a leaf node shows the resultof a decisionThe DT structure used for recognition of 1D ESis shown in Figure 8
Clustering analysis is the task of grouping a set of objectsin such a way that objects in the same cluster are moresimilar to each other than to those in other clusters It isused in many fields including pattern recognition machinelearning and information retrieval In this paper clusteringis mainly used to determine the threshold of decision rulein DT The Euclidean distance criterion is most commonlyemployed in clustering and the Euclidean distance between1199111
= (11991111
1199111119895
) and 1199112
= (11991121
1199112119895
) of instances isdefined as
119889 (1199111 1199112) = radic
119895
sum
119894=1
(1199111119894
minus 1199112119894
)2
(9)
Since there is only one feature applied in each decisionrule the cluster center can be easily obtained by takingthe mean of train data with every AST For simplicity we
EW receiver
Radar
Beam position variation in
elevation
Azimuth
rotation
NB
NP A
Figure 9 A sketch map of NB and NP for 3D radar with 1D ES
select the median of the cluster centers of two leaf nodesas the threshold in each decision Then 119867
119903and 119867V can be
determined and recognition can be carried out according tothe DT in Figure 8
36 Parameter Extraction This paper mainly considers the1D ES of 3D radar which employs a judicious mix of elec-tronic beam steering orderly in elevation and mechanicallymovement in azimuth After identification of the 1D ES fromMS and 2D ES we need to extract some crucial parametersof 1D ES of 3D radar Besides ASP the key parameters of1D electronic scanning are the beam position number inelevation (defined by NB) and pulse number in a beamposition (defined by NP) which are important to estimatethe beam direction of 3D radar as shown in Figure 9 Forsimplicity bothNB andNP are assumed to be constant in thisstudy The NB and NP are 6 and 4 respectively in Figure 9
The parameter extraction of 1D ES of 3D radar is mainlybased on the beam sequence 119886
119898 extracted in Section 33
From the first-order difference sequence of the sequence119886119898
shown in the subgraph of Figure 6(a) we can see thatthere exist a peak and a trough at two sides of every beamposition which result from the beam position variation inelevation Figure 10 depicts the first difference sequence of thesubgraph and its corresponding PA sequence Consequentlywe can extract NP by determining the interval between thehighest peak and the lowest trough of the beam position withmaximum PA Firstly locate the maximum PA of sequence119886119898
denoted by 119886119898
[119872119887] 0 lt 119872
119886lt 119873119889 Obviously 119886
119898[119872119887]
is equal to 119886[119872119886] which is extracted in Section 33 Then as
for the sequence 1198891 locate the nearby highest peak before
1198891[119872119887] (denoted by 119889
1[119872119901
]) and the nearby lowest troughafter 119889
1[119872119887] (denoted by 119889
1[119872119905]) For the example sequence
shown in Figure 5(b) both 1198891[119872119901
] and 1198891[119872119905] are marked
with red circles in Figure 10(b) Hence the pulse number in abeam position can be calculated by 119873119875 = 119872
119905minus 119872119901
In order to extract NB the steer circle in elevation of1D ES that is the pulse number in an elevation circleshould be determined An example of an elevation circlesequence is marked with red in Figure 10(a) Because thebeam sequences of two adjacent elevation circles are similarin amplitude we can compare the entire sequence 119886
119898 with
reference sequence to estimate the elevation circle intervaland normalized cross-correlation coefficients are applied
International Journal of Antennas and Propagation 7
66 662 664 666 668 670
05
1
Time (s)
Nor
mal
ized
PA
Elevation circlesequenceBeam position
sequence
Referencesequence
(a) Normalized PA
120 130 140 150 160 170 180minus1
minus05
0
05
1
Index
1st
diffe
renc
e
Mt
Mp
(b) 1st difference
Figure 10 An extracted PA sequence and first difference sequence for 1D ES
here Firstly we determine the reference sequence After NPextraction the beam position sequence with maximum PAcan be obtained easily namely 119886
119898[119872119901
+ 1] 119886119898
[119872119905]
marked with blue in Figure 10(a) Then we select the PA dataof the maximum beam position and the adjacent beam posi-tions at its both sides as the reference sequence 119910[119898] 119898 =
0 1 119873119910
minus 1 marked with black in Figure 10(a) Hence119910 = 119886
119898[119872119901
+1minus119873119875] 119886119898
[119872119905+119873119875] and119873
119910= 3times119873119875
Calculate the mean square errors (MSE) of sequence 119910 and119886119898
119890119886119910
[119897] = radic
119873119910minus1
sum
119899=0
(119910 [119899] minus 119886119898
[119899 + 119897])2
119897 = 0 1 119873119889
+ 1 minus 119873119910
(10)
The MSE sequence of the example sequence is shownin Figure 11 from which we can see that in sequence 119890
119886119910
there are several troughs which are located where there aremain beam positions Among these troughs the lowest onewhose value is equal to 0 represents themaximummain beamposition namely the reference sequence 119910 Furthermorethe secondary lowest trough represents the adjacent mainbeam position Let 119873
119898denote the interval of the two troughs
which are marked with red circles in Figure 11 so that NB canbe obtained by 119873119861 = 119873
119898119873119875
4 Simulations
In order to illustrate the proposed algorithm for recognitionof 1D ES several simulations are discussed in this section
41 Recognition At first 60 data sequences are generatedfor training of feature extraction and recognition describedin Sections 34 and 35 20 from 1D ES 20 from 2D ESand the remaining from MS All the ES data are generatedby the simulator designed in Section 2 and the MS data bythe antenna scan pattern simulator in literature [23] Whilegenerating the simulated data we have varied some differentparameters (eg periodweightings angles and so on) so thatthe simulated data resemble the actual data from differentrealistic scenarios as much as possible
0 50 100 150 200 250 300 350 4000
05
1
15
2
25
3
Index
MSE
Figure 11 The MSE sequence
Table 1 Recognition results
Input Classification Recognition rate ()1D ES 2D ES MS
1D ES 88 11 1 882D ES 8 92 0 92MS 4 1 95 95
The features extracted from the simulated data sequenceare depicted in Figure 12 We can see that the feature 119862
119903is
distinguishable Most 119862119903values are equal to 1 for both 1D ES
and 2D ES Although the values of 119862119903are above 07 for MS
they are still less than those for ES The threshold 119867119903is set to
088 based on the train data Moreover the 119881119889value for 1D ES
is generally greater than that for 2D ES and the threshold 119867Vis set to 000031
The number of simulated samples is increased to 100for each AST for recognition Table 1 presents the specificrecognition results The data illustrate that the methodproposed in this paper is effective to distinguish ES fromMS The recognition rates of different ASTs are equal to orabove 88 and the classification accuracy for ES (1D and 2D)even reaches 995 It is suggested to refer to literature [13]for further classification of specific MS type such as circularscan sector scan raster scan and helical scan
8 International Journal of Antennas and Propagation
2 4 6 8 10 12 14 16 18 2006
065
07
075
08
085
09
095
1
105
11
Data index
Cr
1D ES2D ESMS
(a) 119862119903
2 4 6 8 10 12 14 16 18 200
02
04
06
08
1
12
14
16
18
2times10minus3
Data index
Vd
1D ES2D ES
(b) 119881119889
Figure 12 The extracted features
Table 2 Simulation scenarios
Scenario MSlowast Scope Inlowastlowast NP extraction rate () NB extraction rate ()1 Circular Yes 95 952 Sector Yes 94 943 Circular No 94 934 Sector No 89 89lowast
Themechanical scanning type applied by 3D radar in azimuthlowastlowastDoes the EW receiver locate in the elevation scope of 3D radar
42 Parameter Extraction In order to validate the parameterextraction for 1D ES 4 different scenarios are considered inthis simulation which are listed in Table 2 When the EWreceiver does not locate in the elevation scope of 3D radar itcan only receive the signals from the side beams of 3D radarThe simulated samples in which the acquired pulses are notsufficient for parameter extraction are aborted
Unlike estimated parameters (eg ASP) which are avail-able in a tolerance range the main parameters extracted inthis paper (NP and NB) will be invalid if the extracted valuesare incorrect Therefore the extraction accuracy is used toevaluate the performance of parameter extraction here Theextraction result is illustrated in Table 2 It can be observedthat the correct extraction rate of NP is almost equal to thatof NB The reason is that the extraction of NB is based onthe result ofNP extractionThe extraction accuracies are over89 and with slight difference for all 4 scenarios It can bededuced that the parameter extraction is effective even by theside beams signals
5 Conclusions
In this paper we studied the problem of recognition andparameter extraction of 1D ES for 3D radar on which noclear and accessible study is available in the open literatureThe main contribution of this work is making a maidenattempt to resolve the problem and providing an effective
method Firstly an ES pattern simulator to synthesize PAdata is designed Then the methodology for recognition andparameter extraction of 1D ES for 3D radar is explained indetail Finally the algorithm is validated by simulation
Our study will be beneficial to 3D radar identificationfor ELINT and ESM systems Moreover it is also usefulfor estimating the beam direction of 3D radar Our mainfollowing work is to validate the algorithm by real radardata acquired by EW receivers To achieve the purpose someexperiments are going to be designed in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by the National Natural ScienceFoundation of China (no 61201336)
References
[1] J Wisniewski and M Mazur ldquoNaval 3D radar in C bandrdquoin Proceedings of the 14th IEEE International Conference onMicrowaves Radar andWireless Communications (MIKON rsquo02)vol 3 pp 843ndash846 2002
International Journal of Antennas and Propagation 9
[2] F Orsini B di Loreto and A Giacomini ldquo3D X-band tacticalacquisition radar (X-TAR 3D)rdquo in Proceedings of the EuropeanRadar Conference pp 589ndash592 October 2009
[3] D L Adamy EW 102 A Second Course in Electronic WarfareArtech House Boston Mass USA 2004
[4] A Irci A Saranli and B Baykal ldquoStudy on Q-RAM andfeasible directions based methods for resource management inphased array radar systemsrdquo IEEE Transactions on Aerospaceand Electronic Systems vol 46 no 4 pp 1848ndash1864 2010
[5] R G Wiley ELINT The Interception and Analysis of RadarSignals Artech House Boston Mass USA 2006
[6] M Zhu K-C Fu X-Y Huang S-D Wu and W-D Jin ldquoAnovel recognition approach for radar emitter signals based onon-line independent support vector machinesrdquo Advances inComputer Science and Its Applications vol 2 no 3 pp 390ndash3962013
[7] D H A Maithripala and S Jayasuriya ldquoRadar deceptionthrough phantom track generationrdquo in Proceedings of theAmerican Control Conference (ACC rsquo05) pp 4102ndash4106 June2005
[8] B Falk ldquoAntenna PatternGeneratorrdquoUS Patent 3540046 1970[9] A B Evan Jr ldquoDigital antenna pattern generator for radar
simulationrdquo US Patent 4008476 1977[10] T E Zaczek ldquoAntenna scan pattern generatorrdquo US Patent
4327417 1982[11] Y-H Kim W-J Kim K-H Song J-W Han and H-N Kim
ldquoModeling of a radar signal for scan patternrdquo in Proceedings ofthe IEEE Military Communications Conference (MILCOM rsquo09)pp 1ndash6 Vancouver Canada October 2009
[12] T H Greer ldquoAutomatic recognition of radar scan typerdquo USPatent 6697007 2004
[13] B Barshan and B Eravci ldquoAutomatic radar antenna scantype recognition in electronic warfarerdquo IEEE Transactions onAerospace and Electronic Systems vol 48 no 4 pp 2908ndash29312012
[14] X-S Wang S-P Xiao D-J Feng and F Zhao Modeling andSimulation of Modern Radar and Electronic Warfare SystemsPublishing House of Electronics Industry Xian China 2010
[15] B R Mahafza and A Z Elsherbeni MATLAB Simulations forRadar Systems Design CRC Press Boca Raton Fla USA 2004
[16] G-X Zhang and X Li ldquoA new recognition system for radaremitter signalsrdquo Kybernetes vol 41 no 9 pp 1351ndash1360 2012
[17] H K Mardia ldquoNew techniques for the deinterleaving ofrepetitive sequencesrdquo IEE proceedings vol 136 no 4 pp 149ndash154 1989
[18] D J Milojevic and B M Popovic ldquoImproved algorithm for thedeinterleaving of radar pulsesrdquo IEE Proceedings F vol 139 no 1pp 98ndash104 1992
[19] D J Nelson ldquoSpecial purpose correlation functions forimproved signal detection and parameter estimationrdquo in Pro-ceedings of the International Conference on Acoustics Speechand Signal Processing (ICASSP rsquo93) pp 73ndash76 1993
[20] P Wei L Qiu and H Wu ldquoThe sorting of radar pulsesignal based on plane transformationrdquo in Proceedings of theInternational Conference on Systems and Informatics (ICSAI rsquo12)pp 326ndash328 May 2012
[21] W-T Lo Y-S Chang R-K Sheu C-C Chiu and S-M YuanldquoCUDT a CUDA based decision tree algorithmrdquoThe ScientificWorld Journal vol 2014 Article ID 745640 12 pages 2014
[22] Q-D Wu B Yan C Zhang L Wang G-B Ning and BYu ldquoDisplacement prediction of tunnel surrounding rock acomparison of support vector machine and artificial neuralnetworkrdquo Mathematical Problems in Engineering vol 2014Article ID 351496 6 pages 2014
[23] B Eravcg Automatic radar antenna scan analysis in electronicwarfare [MS thesis] Department of Electrical and ElectronicsEngineering Bilkent University Ankara Turkey 2010
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
International Journal of Antennas and Propagation 3
Beamsequenceextraction
Normalization ASPestimation
Featuresextraction
1D ESrecognition
Parameterextraction
PA versus TOAdata
Figure 2 The flowchart of the methodology
0 5 10 15 20 25 30
0
Time (s)
PA (d
Bm)
minus60
minus40
minus20
(a)
0 5 10 15 20 25 30Time (s)
0
05
1
Nor
mal
ized
PA
(b)
Figure 3 An example PA versus a TOA signal for 3D radar and its normalized sequence
extracted from the beam sequence by utilizing the RC [16]Finally two vital parameters for 1D ES are extracted namelybeam position number in elevation and pulse number in abeam position
31 Normalization Assume that the received or simulatedPA and TOA data are 119886[119898] and 119905[119898] respectively 119898 =
0 1 119872 minus 1 where 119872 is the pulse number The inputsignal should have at least two cycles of the antenna scan forASP estimation Figure 3(a) shows an example PA versus aTOA signal with three periods from a 3D radar of Hammingweighting type In order to eliminate the influence of propa-gation normalization is needed for PA data While PA dataare in dBW (decibel relative to Watt) scale the maximumvalue of which may be zero the data is transformed fromdBW to Volt (V) first and normalized subsequently Consider
119886119881
[119898] = 10119886[119898]20
119898 = 0 1 119872 minus 1
119886 [119898] =119886119881
[119898]
max (119886119881
) 119898 = 0 1 119872 minus 1
(3)
where max(sdot) stands for the maximum value of the sequencein round brackets In this paper a sequence can also bedenoted by a letter or letters in curly brackets for example119886119881
As a result of normalization the example sequence
in Figure 3(a) is transformed to the sequence shown inFigure 3(b)
32 ASP Estimation The AST analysis is based on PAsequence with only one period so we estimate the ASP firstTheASP estimation algorithmused in literature [13] is appliedin this paper
In modern EW various types of PRI modulation areemployed such as constant jittered staggered sliding wob-ulated and D amp S (Dwell and Switch) [5] It will makethe sampling of PA data nonuniform and result in complexsucceeding processing In order to estimate the ASP it isbetter to resample the received data uniformly
Resampling with the shortest pulse interval is a simplemeasure while it does not take into account the PRI mod-ulation For exactness we determine the sampling intervalafter the analysis of PRI modulation Different PRI analysismethods such as CDIF [17] SDIF [18] PRI transform [19]and plan transform [20] can be used here and differentresampling intervals are chosen for different PRImodulationFor example the signal with stagger-type PRI is resampledwith the highest PRI in the stagger levels [13] and thatwith jitter-type PRI is resampled with the average PRI Itis well known that PRI analysis is crucial to radar signalsorting Consequently resampling signals based on PRImod-ulation information can maintain as many original signalsas possible and reduce the influence of dropped and falsepulses Furthermore the result of PRI analysis can also beuseful in the next section During the resampling processinterpolation is performed if the signal value is not available ata particular instant Different interpolation techniques can beused including nearest point linear polynomial Gaussiansinc or spline interpolation For simplicity we choose nearestpoint interpolation here and the points where no pulses aredetected by receiver are filled with zeros
Suppose that the resampling interval chosen based onthe result of PRI analysis is 119879
119901(for the example signal in
Figure 3 119879119901is chosen to 0002 s) and the resampled sequence
is 119909[119899] 119899 = 0 1 119873 minus 1 where 119873 is the sequence lengthThe normalized autocorrelation coefficients of the
sequence 119909 are calculated to estimate the ASP
119903119909119909
[119897] =sum119882minus1
119899=0119909 [119899] 119909 [119899 + 119897]
radicsum119882minus1
119899=01199092 [119899]radicsum
119882minus1
119899=01199092 [119899 + 119897]
119897 = 0 1 119873 minus 119882
(4)
where 119897 is the lag variable and 119882 is the window length thatis chosen to compromise between computational complexityand accuracy Since the sequence 119909 is assumed to have atleast two periods 119882 is chosen as 1198732 in this paper
The first maximum value of the sequence 119903119909119909
[119897] is foundand the corresponding lag value 119897
119889is assigned as the period
4 International Journal of Antennas and Propagation
0 1000 2000 3000 4000 5000 6000 7000 80000
01
02
03
04
05
06
07
08
09
1
l (lag)
r xx
(l)
Figure 4 Normalized autocorrelation coefficients of examplesequence
Hence the ASP can be obtained by 119879119860
= 119897119889
sdot 119879119901 The normal-
ized autocorrelation coefficients of the example sequence areillustrated in Figure 4 from which we can obtain 119897
119889= 5000
so that 119879119860
= 10 s
33 Beam Sequence Extraction After ASP estimation thesignal of one scan circle can be extracted for followingprocessing During the extraction make sure the circles areintegrated and the main beam is located in the middle ofevery circle Assume that the sequence with one periodextracted from the normalized input signal before resamplingis 119886[119899
1] 119886[119899
2]
When the radar beam steers through the location of theEW receiver in a scanning circle the received PA reachesthe maximum in the period Therefore we can locate themaximum main beam by finding the maximum PA firstIn literature [13] the main beam sequence is extracted byusing the two index points on each side of the maximumpoint where the PA drops to 001mV However it does nottake into account the ES Unlike the analysis of MS whichis based on only main beams that of ES is based on beamgroup including both main beam and adjacent side beamsConsequently the key of beam sequence extraction is to findthe edge of side beams
Because of the quick switch of beam direction to ES thechange of received PA data may be drastic The PA may dropto 001mV or other PA threshold values even during themainbeam scanning hence the method of beam extraction basedon PA threshold is no longer valid In this study we extractthe beam sequence based on received TOA data
The PAs of side beams are commonly much less thanthose of main beam When the PA is below the sensitivitylevel of the EW receiver the pulse will not be interceptedMost signals of side beams except the first side beam maybe undetected by EW receiver As a result there will be someintervals between detected pulses much longer than ordinaryPRIs
In order to determine the edges of side beams we seta PRI threshold 119867
119905according to the PRI analysis result in
Section 32 and then find twonearest PRIs that are longer than119867119905on both sides of the main beam The concrete procedures
of beam sequence extraction are given below
Step 1 Find the maximum PA 119886[119872119886] 1198991
lt 119872119886
lt 1198992 This
step is used to locate the main beam in the scan circleStep 2 Initialization Set bit = 1 119894 = 0Step 3 Compute the PRI on the right side of the main beam
one by one Let 119894 = 119894 + 1 119898 = 119872119886
minus 119894 times bit compute1199051015840
= 119905[119898 + 1] minus 119905[119898]Step 4 If 119905
1015840
lt 119867119905 119867119905being the threshold of pulse interval
repeat Step 3 otherwise if 1199051015840
ge 119867119905 the edge of right
side beam can be determined denote current 119898 by1198981
Step 5 Initialization Set bit = minus1 119894 = 0Step 6 Compute the PRI on the left side of the main beam
one by one Let 119894 = 119894 + 1 119898 = 119872119886
minus 119894 times bit compute1199051015840
= 119905[119898] minus 119905[119898 minus 1]Step 7 If 119905
1015840
lt 119867119905 repeat Step 6 otherwise if 119905
1015840
ge 119867119905 the edge
of left side beam can be determined denote current119898 by 119898
2
Step 8 Extract the beam sequence 119886119898
= 119886[1198981] 119886[119898
1+
1] 119886[1198982
minus 1] 119886[1198982] and the corresponding TOA
sequence 119905[1198981] 119905[1198981
+ 1] 119905[1198982
minus 1] 119905[1198982]
Besides the first side beams some other side beams mayalso be extracted after these steps It will not affect thefollowing recognitionThe beam sequence extracted from theexample sequence in Figure 3 is depicted in Figure 5(a) andFigure 5(b) shows a typical beam sequence of MS extractedby the above procedure
34 Feature Extraction According to the previous analysisthe crucial difference of PA sequence between MS and ESlies at the points where beams switch Since the antennabeam moves inertially for MS the measured PAs changecontinuously For ES the PAs of the same beamposition showslight variation while when beam direction changes theremay be a dramatic change of PAs Therefore it is necessaryto analyze the variations of PA sequence and the processingthat first occurs is calculating the difference sequence
At first the first-order difference sequence of the PA datais calculated
1198891
[119898] = 119886119898
[119898 + 1] minus 119886119898
[119898] 119898 = 0 1 119873119889 (5)
where 119873119889
= 1198982
minus 1198981 The first-order difference sequences
of example sequences in Figure 5 are illustrated in Figure 6It is easy to see that the sequence 119889
1 reflects the amplitude
variation of adjacent pulses For ES the values of most pointsin sequence 119889
1 are close to 0 while at the points where
main or side beams switch the values reach local maxima orminima The maximum occurs at the point where the mainbeam changes to the first side beam and its value is slightlyless than 1 Similarly for MS there are also local maxima orminima where main or side beams switch and the maximumalso occurs at the switch point between the main beam and
International Journal of Antennas and Propagation 5
64 65 66 67 68 69 7 71 72 730
05
1
Time (s)
Nor
mal
ized
PA
(a) ES
0
05
1
Nor
mal
ized
PA
64 65 66 67 68 69 7 71
Time (s)
(b) MS
Figure 5 Beam sequence
0 50 100 150 200 250 300 350 400minus1
minus05
0
05
1
Index
1st
diffe
renc
e
120 140 160 180minus1
0
1
(a) ES
0 50 100 150 200 250 300minus01
0
01
02
03
Index
1st
diffe
renc
e(b) MS
Figure 6 First-order difference sequence
0 50 100 150 200 250 300 350 400
005
1
Index
minus05minus12
nd d
iffer
ence
(a) ES
0 50 100 150 200 250 300
0005
01015
Index
minus0052nd
diff
eren
ce
(b) MS
Figure 7 Second-order difference sequence
side beam What is different is that the sequence 1198891 shows
gradual change at most points for MS and the maximumvalue is much less than 1
Because most values of sequence 1198891 for ES are near 0
we further calculate the second-order difference sequence
1198892
[119898] = 1198891
[119898 + 1] minus 1198891
[119898] 119898 = 0 1 119873119889
minus 1 (6)
Then theremay be slight variation between 1198892[119898] and 119889
1[119898+
1] for most 119898 for ES The second-order difference sequencesof example sequences in Figure 5 are illustrated in Figure 7
In order to accomplish the recognition we haveattempted to study a number of different features to identifyES but the best results were obtained with the featuredescribed below
By comparing the first-order difference sequences inFigure 6 with the second-order ones in Figure 7 we foundthat there are some notable differences between ES and MSFor ES the first- and second-order difference sequences seemsimilar at most points except the points where main or sidebeams change while for MS the two sequences are quite
differentTherefore we select RC as the feature to distinguishMS and ES
Definition 1 Suppose that discrete signal sequences 1198781(119894) 119894 =
0 1 119873 and 1198782(119895) 119895 = 0 1 119873 are one-dimensional
and nonnegative that is 1198781(119894) ge 0 119878
2(119894) ge 0 119894 = 0 1 119873
RC of 1198781 and 119878
2 is defined as
119862119903
=sum 1198781
(119894) 1198782
(119894)
(radicsum 1198782
1(119894) sdot radicsum 119878
2
2(119894))
(7)
where all points of signal sequences 1198781(119894) and 119878
2(119895) are not
0 Obviously we can get 0 le 119862119903
le 1In order to obtain the RC from the two difference
sequences we set 1198782(119894) = |119889
2(119894)| 119894 = 0 1 119873
119889minus 1 and
1198781 is given by the following formula
1198781
(119894) = max (10038161003816100381610038161198891 (119894 + 1)
1003816100381610038161003816 10038161003816100381610038161198892 (119894)
1003816100381610038161003816) 119894 = 0 1 119873119889
minus 1
(8)
Then the RC can be calculated using formula (7) The valueof 119862119903is large for ES and nearly equal to 1 while it is much less
for MS
6 International Journal of Antennas and Propagation
Features
Cr gt Hr Cr Hr
ES MS
Vd gt H Vd H
1D ES 2D ES
⩽
⩽
Figure 8 The DT structure for recognition of 1D ES
Furthermore in order to differentiate 1D ES from 2D ESwe further process the first-order difference sequence 119889
1
First pick up the elements less than 119867119886in sequence |119889
1|
119867119886is the threshold Hence the picked points present the
variations among a beam position for ES Then compute thevariance of picked elements which is denoted by 119881
119889 Because
there is no beam movement among a beam position for 2DES 119881119889of 2D ES is commonly less than that for 1D ES for
which beam still moves inertially in azimuth
35 Recognition The recognition of 1D ES has two stagesfirst distinguishing ES from MS and then distinguishing 1DES from 2D ES
After extracting the two features we can accomplishthe recognition by utilizing a classifier such as naive Bayesdecision tree (DT) [21] clustering analysis artificial neuralnetworks [22] and support vectormachine [22] Based on therecognition procedure in this paper a DT classifier based onclustering analysis is chosen to recognize 1D ES
A DT classifier is one of the most intuitive and naturalclassifiers and it is fast comprehensible and easy to visualizeA DT consists of two kinds of nodes an internal noderepresents a decision rule and a leaf node shows the resultof a decisionThe DT structure used for recognition of 1D ESis shown in Figure 8
Clustering analysis is the task of grouping a set of objectsin such a way that objects in the same cluster are moresimilar to each other than to those in other clusters It isused in many fields including pattern recognition machinelearning and information retrieval In this paper clusteringis mainly used to determine the threshold of decision rulein DT The Euclidean distance criterion is most commonlyemployed in clustering and the Euclidean distance between1199111
= (11991111
1199111119895
) and 1199112
= (11991121
1199112119895
) of instances isdefined as
119889 (1199111 1199112) = radic
119895
sum
119894=1
(1199111119894
minus 1199112119894
)2
(9)
Since there is only one feature applied in each decisionrule the cluster center can be easily obtained by takingthe mean of train data with every AST For simplicity we
EW receiver
Radar
Beam position variation in
elevation
Azimuth
rotation
NB
NP A
Figure 9 A sketch map of NB and NP for 3D radar with 1D ES
select the median of the cluster centers of two leaf nodesas the threshold in each decision Then 119867
119903and 119867V can be
determined and recognition can be carried out according tothe DT in Figure 8
36 Parameter Extraction This paper mainly considers the1D ES of 3D radar which employs a judicious mix of elec-tronic beam steering orderly in elevation and mechanicallymovement in azimuth After identification of the 1D ES fromMS and 2D ES we need to extract some crucial parametersof 1D ES of 3D radar Besides ASP the key parameters of1D electronic scanning are the beam position number inelevation (defined by NB) and pulse number in a beamposition (defined by NP) which are important to estimatethe beam direction of 3D radar as shown in Figure 9 Forsimplicity bothNB andNP are assumed to be constant in thisstudy The NB and NP are 6 and 4 respectively in Figure 9
The parameter extraction of 1D ES of 3D radar is mainlybased on the beam sequence 119886
119898 extracted in Section 33
From the first-order difference sequence of the sequence119886119898
shown in the subgraph of Figure 6(a) we can see thatthere exist a peak and a trough at two sides of every beamposition which result from the beam position variation inelevation Figure 10 depicts the first difference sequence of thesubgraph and its corresponding PA sequence Consequentlywe can extract NP by determining the interval between thehighest peak and the lowest trough of the beam position withmaximum PA Firstly locate the maximum PA of sequence119886119898
denoted by 119886119898
[119872119887] 0 lt 119872
119886lt 119873119889 Obviously 119886
119898[119872119887]
is equal to 119886[119872119886] which is extracted in Section 33 Then as
for the sequence 1198891 locate the nearby highest peak before
1198891[119872119887] (denoted by 119889
1[119872119901
]) and the nearby lowest troughafter 119889
1[119872119887] (denoted by 119889
1[119872119905]) For the example sequence
shown in Figure 5(b) both 1198891[119872119901
] and 1198891[119872119905] are marked
with red circles in Figure 10(b) Hence the pulse number in abeam position can be calculated by 119873119875 = 119872
119905minus 119872119901
In order to extract NB the steer circle in elevation of1D ES that is the pulse number in an elevation circleshould be determined An example of an elevation circlesequence is marked with red in Figure 10(a) Because thebeam sequences of two adjacent elevation circles are similarin amplitude we can compare the entire sequence 119886
119898 with
reference sequence to estimate the elevation circle intervaland normalized cross-correlation coefficients are applied
International Journal of Antennas and Propagation 7
66 662 664 666 668 670
05
1
Time (s)
Nor
mal
ized
PA
Elevation circlesequenceBeam position
sequence
Referencesequence
(a) Normalized PA
120 130 140 150 160 170 180minus1
minus05
0
05
1
Index
1st
diffe
renc
e
Mt
Mp
(b) 1st difference
Figure 10 An extracted PA sequence and first difference sequence for 1D ES
here Firstly we determine the reference sequence After NPextraction the beam position sequence with maximum PAcan be obtained easily namely 119886
119898[119872119901
+ 1] 119886119898
[119872119905]
marked with blue in Figure 10(a) Then we select the PA dataof the maximum beam position and the adjacent beam posi-tions at its both sides as the reference sequence 119910[119898] 119898 =
0 1 119873119910
minus 1 marked with black in Figure 10(a) Hence119910 = 119886
119898[119872119901
+1minus119873119875] 119886119898
[119872119905+119873119875] and119873
119910= 3times119873119875
Calculate the mean square errors (MSE) of sequence 119910 and119886119898
119890119886119910
[119897] = radic
119873119910minus1
sum
119899=0
(119910 [119899] minus 119886119898
[119899 + 119897])2
119897 = 0 1 119873119889
+ 1 minus 119873119910
(10)
The MSE sequence of the example sequence is shownin Figure 11 from which we can see that in sequence 119890
119886119910
there are several troughs which are located where there aremain beam positions Among these troughs the lowest onewhose value is equal to 0 represents themaximummain beamposition namely the reference sequence 119910 Furthermorethe secondary lowest trough represents the adjacent mainbeam position Let 119873
119898denote the interval of the two troughs
which are marked with red circles in Figure 11 so that NB canbe obtained by 119873119861 = 119873
119898119873119875
4 Simulations
In order to illustrate the proposed algorithm for recognitionof 1D ES several simulations are discussed in this section
41 Recognition At first 60 data sequences are generatedfor training of feature extraction and recognition describedin Sections 34 and 35 20 from 1D ES 20 from 2D ESand the remaining from MS All the ES data are generatedby the simulator designed in Section 2 and the MS data bythe antenna scan pattern simulator in literature [23] Whilegenerating the simulated data we have varied some differentparameters (eg periodweightings angles and so on) so thatthe simulated data resemble the actual data from differentrealistic scenarios as much as possible
0 50 100 150 200 250 300 350 4000
05
1
15
2
25
3
Index
MSE
Figure 11 The MSE sequence
Table 1 Recognition results
Input Classification Recognition rate ()1D ES 2D ES MS
1D ES 88 11 1 882D ES 8 92 0 92MS 4 1 95 95
The features extracted from the simulated data sequenceare depicted in Figure 12 We can see that the feature 119862
119903is
distinguishable Most 119862119903values are equal to 1 for both 1D ES
and 2D ES Although the values of 119862119903are above 07 for MS
they are still less than those for ES The threshold 119867119903is set to
088 based on the train data Moreover the 119881119889value for 1D ES
is generally greater than that for 2D ES and the threshold 119867Vis set to 000031
The number of simulated samples is increased to 100for each AST for recognition Table 1 presents the specificrecognition results The data illustrate that the methodproposed in this paper is effective to distinguish ES fromMS The recognition rates of different ASTs are equal to orabove 88 and the classification accuracy for ES (1D and 2D)even reaches 995 It is suggested to refer to literature [13]for further classification of specific MS type such as circularscan sector scan raster scan and helical scan
8 International Journal of Antennas and Propagation
2 4 6 8 10 12 14 16 18 2006
065
07
075
08
085
09
095
1
105
11
Data index
Cr
1D ES2D ESMS
(a) 119862119903
2 4 6 8 10 12 14 16 18 200
02
04
06
08
1
12
14
16
18
2times10minus3
Data index
Vd
1D ES2D ES
(b) 119881119889
Figure 12 The extracted features
Table 2 Simulation scenarios
Scenario MSlowast Scope Inlowastlowast NP extraction rate () NB extraction rate ()1 Circular Yes 95 952 Sector Yes 94 943 Circular No 94 934 Sector No 89 89lowast
Themechanical scanning type applied by 3D radar in azimuthlowastlowastDoes the EW receiver locate in the elevation scope of 3D radar
42 Parameter Extraction In order to validate the parameterextraction for 1D ES 4 different scenarios are considered inthis simulation which are listed in Table 2 When the EWreceiver does not locate in the elevation scope of 3D radar itcan only receive the signals from the side beams of 3D radarThe simulated samples in which the acquired pulses are notsufficient for parameter extraction are aborted
Unlike estimated parameters (eg ASP) which are avail-able in a tolerance range the main parameters extracted inthis paper (NP and NB) will be invalid if the extracted valuesare incorrect Therefore the extraction accuracy is used toevaluate the performance of parameter extraction here Theextraction result is illustrated in Table 2 It can be observedthat the correct extraction rate of NP is almost equal to thatof NB The reason is that the extraction of NB is based onthe result ofNP extractionThe extraction accuracies are over89 and with slight difference for all 4 scenarios It can bededuced that the parameter extraction is effective even by theside beams signals
5 Conclusions
In this paper we studied the problem of recognition andparameter extraction of 1D ES for 3D radar on which noclear and accessible study is available in the open literatureThe main contribution of this work is making a maidenattempt to resolve the problem and providing an effective
method Firstly an ES pattern simulator to synthesize PAdata is designed Then the methodology for recognition andparameter extraction of 1D ES for 3D radar is explained indetail Finally the algorithm is validated by simulation
Our study will be beneficial to 3D radar identificationfor ELINT and ESM systems Moreover it is also usefulfor estimating the beam direction of 3D radar Our mainfollowing work is to validate the algorithm by real radardata acquired by EW receivers To achieve the purpose someexperiments are going to be designed in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by the National Natural ScienceFoundation of China (no 61201336)
References
[1] J Wisniewski and M Mazur ldquoNaval 3D radar in C bandrdquoin Proceedings of the 14th IEEE International Conference onMicrowaves Radar andWireless Communications (MIKON rsquo02)vol 3 pp 843ndash846 2002
International Journal of Antennas and Propagation 9
[2] F Orsini B di Loreto and A Giacomini ldquo3D X-band tacticalacquisition radar (X-TAR 3D)rdquo in Proceedings of the EuropeanRadar Conference pp 589ndash592 October 2009
[3] D L Adamy EW 102 A Second Course in Electronic WarfareArtech House Boston Mass USA 2004
[4] A Irci A Saranli and B Baykal ldquoStudy on Q-RAM andfeasible directions based methods for resource management inphased array radar systemsrdquo IEEE Transactions on Aerospaceand Electronic Systems vol 46 no 4 pp 1848ndash1864 2010
[5] R G Wiley ELINT The Interception and Analysis of RadarSignals Artech House Boston Mass USA 2006
[6] M Zhu K-C Fu X-Y Huang S-D Wu and W-D Jin ldquoAnovel recognition approach for radar emitter signals based onon-line independent support vector machinesrdquo Advances inComputer Science and Its Applications vol 2 no 3 pp 390ndash3962013
[7] D H A Maithripala and S Jayasuriya ldquoRadar deceptionthrough phantom track generationrdquo in Proceedings of theAmerican Control Conference (ACC rsquo05) pp 4102ndash4106 June2005
[8] B Falk ldquoAntenna PatternGeneratorrdquoUS Patent 3540046 1970[9] A B Evan Jr ldquoDigital antenna pattern generator for radar
simulationrdquo US Patent 4008476 1977[10] T E Zaczek ldquoAntenna scan pattern generatorrdquo US Patent
4327417 1982[11] Y-H Kim W-J Kim K-H Song J-W Han and H-N Kim
ldquoModeling of a radar signal for scan patternrdquo in Proceedings ofthe IEEE Military Communications Conference (MILCOM rsquo09)pp 1ndash6 Vancouver Canada October 2009
[12] T H Greer ldquoAutomatic recognition of radar scan typerdquo USPatent 6697007 2004
[13] B Barshan and B Eravci ldquoAutomatic radar antenna scantype recognition in electronic warfarerdquo IEEE Transactions onAerospace and Electronic Systems vol 48 no 4 pp 2908ndash29312012
[14] X-S Wang S-P Xiao D-J Feng and F Zhao Modeling andSimulation of Modern Radar and Electronic Warfare SystemsPublishing House of Electronics Industry Xian China 2010
[15] B R Mahafza and A Z Elsherbeni MATLAB Simulations forRadar Systems Design CRC Press Boca Raton Fla USA 2004
[16] G-X Zhang and X Li ldquoA new recognition system for radaremitter signalsrdquo Kybernetes vol 41 no 9 pp 1351ndash1360 2012
[17] H K Mardia ldquoNew techniques for the deinterleaving ofrepetitive sequencesrdquo IEE proceedings vol 136 no 4 pp 149ndash154 1989
[18] D J Milojevic and B M Popovic ldquoImproved algorithm for thedeinterleaving of radar pulsesrdquo IEE Proceedings F vol 139 no 1pp 98ndash104 1992
[19] D J Nelson ldquoSpecial purpose correlation functions forimproved signal detection and parameter estimationrdquo in Pro-ceedings of the International Conference on Acoustics Speechand Signal Processing (ICASSP rsquo93) pp 73ndash76 1993
[20] P Wei L Qiu and H Wu ldquoThe sorting of radar pulsesignal based on plane transformationrdquo in Proceedings of theInternational Conference on Systems and Informatics (ICSAI rsquo12)pp 326ndash328 May 2012
[21] W-T Lo Y-S Chang R-K Sheu C-C Chiu and S-M YuanldquoCUDT a CUDA based decision tree algorithmrdquoThe ScientificWorld Journal vol 2014 Article ID 745640 12 pages 2014
[22] Q-D Wu B Yan C Zhang L Wang G-B Ning and BYu ldquoDisplacement prediction of tunnel surrounding rock acomparison of support vector machine and artificial neuralnetworkrdquo Mathematical Problems in Engineering vol 2014Article ID 351496 6 pages 2014
[23] B Eravcg Automatic radar antenna scan analysis in electronicwarfare [MS thesis] Department of Electrical and ElectronicsEngineering Bilkent University Ankara Turkey 2010
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
4 International Journal of Antennas and Propagation
0 1000 2000 3000 4000 5000 6000 7000 80000
01
02
03
04
05
06
07
08
09
1
l (lag)
r xx
(l)
Figure 4 Normalized autocorrelation coefficients of examplesequence
Hence the ASP can be obtained by 119879119860
= 119897119889
sdot 119879119901 The normal-
ized autocorrelation coefficients of the example sequence areillustrated in Figure 4 from which we can obtain 119897
119889= 5000
so that 119879119860
= 10 s
33 Beam Sequence Extraction After ASP estimation thesignal of one scan circle can be extracted for followingprocessing During the extraction make sure the circles areintegrated and the main beam is located in the middle ofevery circle Assume that the sequence with one periodextracted from the normalized input signal before resamplingis 119886[119899
1] 119886[119899
2]
When the radar beam steers through the location of theEW receiver in a scanning circle the received PA reachesthe maximum in the period Therefore we can locate themaximum main beam by finding the maximum PA firstIn literature [13] the main beam sequence is extracted byusing the two index points on each side of the maximumpoint where the PA drops to 001mV However it does nottake into account the ES Unlike the analysis of MS whichis based on only main beams that of ES is based on beamgroup including both main beam and adjacent side beamsConsequently the key of beam sequence extraction is to findthe edge of side beams
Because of the quick switch of beam direction to ES thechange of received PA data may be drastic The PA may dropto 001mV or other PA threshold values even during themainbeam scanning hence the method of beam extraction basedon PA threshold is no longer valid In this study we extractthe beam sequence based on received TOA data
The PAs of side beams are commonly much less thanthose of main beam When the PA is below the sensitivitylevel of the EW receiver the pulse will not be interceptedMost signals of side beams except the first side beam maybe undetected by EW receiver As a result there will be someintervals between detected pulses much longer than ordinaryPRIs
In order to determine the edges of side beams we seta PRI threshold 119867
119905according to the PRI analysis result in
Section 32 and then find twonearest PRIs that are longer than119867119905on both sides of the main beam The concrete procedures
of beam sequence extraction are given below
Step 1 Find the maximum PA 119886[119872119886] 1198991
lt 119872119886
lt 1198992 This
step is used to locate the main beam in the scan circleStep 2 Initialization Set bit = 1 119894 = 0Step 3 Compute the PRI on the right side of the main beam
one by one Let 119894 = 119894 + 1 119898 = 119872119886
minus 119894 times bit compute1199051015840
= 119905[119898 + 1] minus 119905[119898]Step 4 If 119905
1015840
lt 119867119905 119867119905being the threshold of pulse interval
repeat Step 3 otherwise if 1199051015840
ge 119867119905 the edge of right
side beam can be determined denote current 119898 by1198981
Step 5 Initialization Set bit = minus1 119894 = 0Step 6 Compute the PRI on the left side of the main beam
one by one Let 119894 = 119894 + 1 119898 = 119872119886
minus 119894 times bit compute1199051015840
= 119905[119898] minus 119905[119898 minus 1]Step 7 If 119905
1015840
lt 119867119905 repeat Step 6 otherwise if 119905
1015840
ge 119867119905 the edge
of left side beam can be determined denote current119898 by 119898
2
Step 8 Extract the beam sequence 119886119898
= 119886[1198981] 119886[119898
1+
1] 119886[1198982
minus 1] 119886[1198982] and the corresponding TOA
sequence 119905[1198981] 119905[1198981
+ 1] 119905[1198982
minus 1] 119905[1198982]
Besides the first side beams some other side beams mayalso be extracted after these steps It will not affect thefollowing recognitionThe beam sequence extracted from theexample sequence in Figure 3 is depicted in Figure 5(a) andFigure 5(b) shows a typical beam sequence of MS extractedby the above procedure
34 Feature Extraction According to the previous analysisthe crucial difference of PA sequence between MS and ESlies at the points where beams switch Since the antennabeam moves inertially for MS the measured PAs changecontinuously For ES the PAs of the same beamposition showslight variation while when beam direction changes theremay be a dramatic change of PAs Therefore it is necessaryto analyze the variations of PA sequence and the processingthat first occurs is calculating the difference sequence
At first the first-order difference sequence of the PA datais calculated
1198891
[119898] = 119886119898
[119898 + 1] minus 119886119898
[119898] 119898 = 0 1 119873119889 (5)
where 119873119889
= 1198982
minus 1198981 The first-order difference sequences
of example sequences in Figure 5 are illustrated in Figure 6It is easy to see that the sequence 119889
1 reflects the amplitude
variation of adjacent pulses For ES the values of most pointsin sequence 119889
1 are close to 0 while at the points where
main or side beams switch the values reach local maxima orminima The maximum occurs at the point where the mainbeam changes to the first side beam and its value is slightlyless than 1 Similarly for MS there are also local maxima orminima where main or side beams switch and the maximumalso occurs at the switch point between the main beam and
International Journal of Antennas and Propagation 5
64 65 66 67 68 69 7 71 72 730
05
1
Time (s)
Nor
mal
ized
PA
(a) ES
0
05
1
Nor
mal
ized
PA
64 65 66 67 68 69 7 71
Time (s)
(b) MS
Figure 5 Beam sequence
0 50 100 150 200 250 300 350 400minus1
minus05
0
05
1
Index
1st
diffe
renc
e
120 140 160 180minus1
0
1
(a) ES
0 50 100 150 200 250 300minus01
0
01
02
03
Index
1st
diffe
renc
e(b) MS
Figure 6 First-order difference sequence
0 50 100 150 200 250 300 350 400
005
1
Index
minus05minus12
nd d
iffer
ence
(a) ES
0 50 100 150 200 250 300
0005
01015
Index
minus0052nd
diff
eren
ce
(b) MS
Figure 7 Second-order difference sequence
side beam What is different is that the sequence 1198891 shows
gradual change at most points for MS and the maximumvalue is much less than 1
Because most values of sequence 1198891 for ES are near 0
we further calculate the second-order difference sequence
1198892
[119898] = 1198891
[119898 + 1] minus 1198891
[119898] 119898 = 0 1 119873119889
minus 1 (6)
Then theremay be slight variation between 1198892[119898] and 119889
1[119898+
1] for most 119898 for ES The second-order difference sequencesof example sequences in Figure 5 are illustrated in Figure 7
In order to accomplish the recognition we haveattempted to study a number of different features to identifyES but the best results were obtained with the featuredescribed below
By comparing the first-order difference sequences inFigure 6 with the second-order ones in Figure 7 we foundthat there are some notable differences between ES and MSFor ES the first- and second-order difference sequences seemsimilar at most points except the points where main or sidebeams change while for MS the two sequences are quite
differentTherefore we select RC as the feature to distinguishMS and ES
Definition 1 Suppose that discrete signal sequences 1198781(119894) 119894 =
0 1 119873 and 1198782(119895) 119895 = 0 1 119873 are one-dimensional
and nonnegative that is 1198781(119894) ge 0 119878
2(119894) ge 0 119894 = 0 1 119873
RC of 1198781 and 119878
2 is defined as
119862119903
=sum 1198781
(119894) 1198782
(119894)
(radicsum 1198782
1(119894) sdot radicsum 119878
2
2(119894))
(7)
where all points of signal sequences 1198781(119894) and 119878
2(119895) are not
0 Obviously we can get 0 le 119862119903
le 1In order to obtain the RC from the two difference
sequences we set 1198782(119894) = |119889
2(119894)| 119894 = 0 1 119873
119889minus 1 and
1198781 is given by the following formula
1198781
(119894) = max (10038161003816100381610038161198891 (119894 + 1)
1003816100381610038161003816 10038161003816100381610038161198892 (119894)
1003816100381610038161003816) 119894 = 0 1 119873119889
minus 1
(8)
Then the RC can be calculated using formula (7) The valueof 119862119903is large for ES and nearly equal to 1 while it is much less
for MS
6 International Journal of Antennas and Propagation
Features
Cr gt Hr Cr Hr
ES MS
Vd gt H Vd H
1D ES 2D ES
⩽
⩽
Figure 8 The DT structure for recognition of 1D ES
Furthermore in order to differentiate 1D ES from 2D ESwe further process the first-order difference sequence 119889
1
First pick up the elements less than 119867119886in sequence |119889
1|
119867119886is the threshold Hence the picked points present the
variations among a beam position for ES Then compute thevariance of picked elements which is denoted by 119881
119889 Because
there is no beam movement among a beam position for 2DES 119881119889of 2D ES is commonly less than that for 1D ES for
which beam still moves inertially in azimuth
35 Recognition The recognition of 1D ES has two stagesfirst distinguishing ES from MS and then distinguishing 1DES from 2D ES
After extracting the two features we can accomplishthe recognition by utilizing a classifier such as naive Bayesdecision tree (DT) [21] clustering analysis artificial neuralnetworks [22] and support vectormachine [22] Based on therecognition procedure in this paper a DT classifier based onclustering analysis is chosen to recognize 1D ES
A DT classifier is one of the most intuitive and naturalclassifiers and it is fast comprehensible and easy to visualizeA DT consists of two kinds of nodes an internal noderepresents a decision rule and a leaf node shows the resultof a decisionThe DT structure used for recognition of 1D ESis shown in Figure 8
Clustering analysis is the task of grouping a set of objectsin such a way that objects in the same cluster are moresimilar to each other than to those in other clusters It isused in many fields including pattern recognition machinelearning and information retrieval In this paper clusteringis mainly used to determine the threshold of decision rulein DT The Euclidean distance criterion is most commonlyemployed in clustering and the Euclidean distance between1199111
= (11991111
1199111119895
) and 1199112
= (11991121
1199112119895
) of instances isdefined as
119889 (1199111 1199112) = radic
119895
sum
119894=1
(1199111119894
minus 1199112119894
)2
(9)
Since there is only one feature applied in each decisionrule the cluster center can be easily obtained by takingthe mean of train data with every AST For simplicity we
EW receiver
Radar
Beam position variation in
elevation
Azimuth
rotation
NB
NP A
Figure 9 A sketch map of NB and NP for 3D radar with 1D ES
select the median of the cluster centers of two leaf nodesas the threshold in each decision Then 119867
119903and 119867V can be
determined and recognition can be carried out according tothe DT in Figure 8
36 Parameter Extraction This paper mainly considers the1D ES of 3D radar which employs a judicious mix of elec-tronic beam steering orderly in elevation and mechanicallymovement in azimuth After identification of the 1D ES fromMS and 2D ES we need to extract some crucial parametersof 1D ES of 3D radar Besides ASP the key parameters of1D electronic scanning are the beam position number inelevation (defined by NB) and pulse number in a beamposition (defined by NP) which are important to estimatethe beam direction of 3D radar as shown in Figure 9 Forsimplicity bothNB andNP are assumed to be constant in thisstudy The NB and NP are 6 and 4 respectively in Figure 9
The parameter extraction of 1D ES of 3D radar is mainlybased on the beam sequence 119886
119898 extracted in Section 33
From the first-order difference sequence of the sequence119886119898
shown in the subgraph of Figure 6(a) we can see thatthere exist a peak and a trough at two sides of every beamposition which result from the beam position variation inelevation Figure 10 depicts the first difference sequence of thesubgraph and its corresponding PA sequence Consequentlywe can extract NP by determining the interval between thehighest peak and the lowest trough of the beam position withmaximum PA Firstly locate the maximum PA of sequence119886119898
denoted by 119886119898
[119872119887] 0 lt 119872
119886lt 119873119889 Obviously 119886
119898[119872119887]
is equal to 119886[119872119886] which is extracted in Section 33 Then as
for the sequence 1198891 locate the nearby highest peak before
1198891[119872119887] (denoted by 119889
1[119872119901
]) and the nearby lowest troughafter 119889
1[119872119887] (denoted by 119889
1[119872119905]) For the example sequence
shown in Figure 5(b) both 1198891[119872119901
] and 1198891[119872119905] are marked
with red circles in Figure 10(b) Hence the pulse number in abeam position can be calculated by 119873119875 = 119872
119905minus 119872119901
In order to extract NB the steer circle in elevation of1D ES that is the pulse number in an elevation circleshould be determined An example of an elevation circlesequence is marked with red in Figure 10(a) Because thebeam sequences of two adjacent elevation circles are similarin amplitude we can compare the entire sequence 119886
119898 with
reference sequence to estimate the elevation circle intervaland normalized cross-correlation coefficients are applied
International Journal of Antennas and Propagation 7
66 662 664 666 668 670
05
1
Time (s)
Nor
mal
ized
PA
Elevation circlesequenceBeam position
sequence
Referencesequence
(a) Normalized PA
120 130 140 150 160 170 180minus1
minus05
0
05
1
Index
1st
diffe
renc
e
Mt
Mp
(b) 1st difference
Figure 10 An extracted PA sequence and first difference sequence for 1D ES
here Firstly we determine the reference sequence After NPextraction the beam position sequence with maximum PAcan be obtained easily namely 119886
119898[119872119901
+ 1] 119886119898
[119872119905]
marked with blue in Figure 10(a) Then we select the PA dataof the maximum beam position and the adjacent beam posi-tions at its both sides as the reference sequence 119910[119898] 119898 =
0 1 119873119910
minus 1 marked with black in Figure 10(a) Hence119910 = 119886
119898[119872119901
+1minus119873119875] 119886119898
[119872119905+119873119875] and119873
119910= 3times119873119875
Calculate the mean square errors (MSE) of sequence 119910 and119886119898
119890119886119910
[119897] = radic
119873119910minus1
sum
119899=0
(119910 [119899] minus 119886119898
[119899 + 119897])2
119897 = 0 1 119873119889
+ 1 minus 119873119910
(10)
The MSE sequence of the example sequence is shownin Figure 11 from which we can see that in sequence 119890
119886119910
there are several troughs which are located where there aremain beam positions Among these troughs the lowest onewhose value is equal to 0 represents themaximummain beamposition namely the reference sequence 119910 Furthermorethe secondary lowest trough represents the adjacent mainbeam position Let 119873
119898denote the interval of the two troughs
which are marked with red circles in Figure 11 so that NB canbe obtained by 119873119861 = 119873
119898119873119875
4 Simulations
In order to illustrate the proposed algorithm for recognitionof 1D ES several simulations are discussed in this section
41 Recognition At first 60 data sequences are generatedfor training of feature extraction and recognition describedin Sections 34 and 35 20 from 1D ES 20 from 2D ESand the remaining from MS All the ES data are generatedby the simulator designed in Section 2 and the MS data bythe antenna scan pattern simulator in literature [23] Whilegenerating the simulated data we have varied some differentparameters (eg periodweightings angles and so on) so thatthe simulated data resemble the actual data from differentrealistic scenarios as much as possible
0 50 100 150 200 250 300 350 4000
05
1
15
2
25
3
Index
MSE
Figure 11 The MSE sequence
Table 1 Recognition results
Input Classification Recognition rate ()1D ES 2D ES MS
1D ES 88 11 1 882D ES 8 92 0 92MS 4 1 95 95
The features extracted from the simulated data sequenceare depicted in Figure 12 We can see that the feature 119862
119903is
distinguishable Most 119862119903values are equal to 1 for both 1D ES
and 2D ES Although the values of 119862119903are above 07 for MS
they are still less than those for ES The threshold 119867119903is set to
088 based on the train data Moreover the 119881119889value for 1D ES
is generally greater than that for 2D ES and the threshold 119867Vis set to 000031
The number of simulated samples is increased to 100for each AST for recognition Table 1 presents the specificrecognition results The data illustrate that the methodproposed in this paper is effective to distinguish ES fromMS The recognition rates of different ASTs are equal to orabove 88 and the classification accuracy for ES (1D and 2D)even reaches 995 It is suggested to refer to literature [13]for further classification of specific MS type such as circularscan sector scan raster scan and helical scan
8 International Journal of Antennas and Propagation
2 4 6 8 10 12 14 16 18 2006
065
07
075
08
085
09
095
1
105
11
Data index
Cr
1D ES2D ESMS
(a) 119862119903
2 4 6 8 10 12 14 16 18 200
02
04
06
08
1
12
14
16
18
2times10minus3
Data index
Vd
1D ES2D ES
(b) 119881119889
Figure 12 The extracted features
Table 2 Simulation scenarios
Scenario MSlowast Scope Inlowastlowast NP extraction rate () NB extraction rate ()1 Circular Yes 95 952 Sector Yes 94 943 Circular No 94 934 Sector No 89 89lowast
Themechanical scanning type applied by 3D radar in azimuthlowastlowastDoes the EW receiver locate in the elevation scope of 3D radar
42 Parameter Extraction In order to validate the parameterextraction for 1D ES 4 different scenarios are considered inthis simulation which are listed in Table 2 When the EWreceiver does not locate in the elevation scope of 3D radar itcan only receive the signals from the side beams of 3D radarThe simulated samples in which the acquired pulses are notsufficient for parameter extraction are aborted
Unlike estimated parameters (eg ASP) which are avail-able in a tolerance range the main parameters extracted inthis paper (NP and NB) will be invalid if the extracted valuesare incorrect Therefore the extraction accuracy is used toevaluate the performance of parameter extraction here Theextraction result is illustrated in Table 2 It can be observedthat the correct extraction rate of NP is almost equal to thatof NB The reason is that the extraction of NB is based onthe result ofNP extractionThe extraction accuracies are over89 and with slight difference for all 4 scenarios It can bededuced that the parameter extraction is effective even by theside beams signals
5 Conclusions
In this paper we studied the problem of recognition andparameter extraction of 1D ES for 3D radar on which noclear and accessible study is available in the open literatureThe main contribution of this work is making a maidenattempt to resolve the problem and providing an effective
method Firstly an ES pattern simulator to synthesize PAdata is designed Then the methodology for recognition andparameter extraction of 1D ES for 3D radar is explained indetail Finally the algorithm is validated by simulation
Our study will be beneficial to 3D radar identificationfor ELINT and ESM systems Moreover it is also usefulfor estimating the beam direction of 3D radar Our mainfollowing work is to validate the algorithm by real radardata acquired by EW receivers To achieve the purpose someexperiments are going to be designed in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by the National Natural ScienceFoundation of China (no 61201336)
References
[1] J Wisniewski and M Mazur ldquoNaval 3D radar in C bandrdquoin Proceedings of the 14th IEEE International Conference onMicrowaves Radar andWireless Communications (MIKON rsquo02)vol 3 pp 843ndash846 2002
International Journal of Antennas and Propagation 9
[2] F Orsini B di Loreto and A Giacomini ldquo3D X-band tacticalacquisition radar (X-TAR 3D)rdquo in Proceedings of the EuropeanRadar Conference pp 589ndash592 October 2009
[3] D L Adamy EW 102 A Second Course in Electronic WarfareArtech House Boston Mass USA 2004
[4] A Irci A Saranli and B Baykal ldquoStudy on Q-RAM andfeasible directions based methods for resource management inphased array radar systemsrdquo IEEE Transactions on Aerospaceand Electronic Systems vol 46 no 4 pp 1848ndash1864 2010
[5] R G Wiley ELINT The Interception and Analysis of RadarSignals Artech House Boston Mass USA 2006
[6] M Zhu K-C Fu X-Y Huang S-D Wu and W-D Jin ldquoAnovel recognition approach for radar emitter signals based onon-line independent support vector machinesrdquo Advances inComputer Science and Its Applications vol 2 no 3 pp 390ndash3962013
[7] D H A Maithripala and S Jayasuriya ldquoRadar deceptionthrough phantom track generationrdquo in Proceedings of theAmerican Control Conference (ACC rsquo05) pp 4102ndash4106 June2005
[8] B Falk ldquoAntenna PatternGeneratorrdquoUS Patent 3540046 1970[9] A B Evan Jr ldquoDigital antenna pattern generator for radar
simulationrdquo US Patent 4008476 1977[10] T E Zaczek ldquoAntenna scan pattern generatorrdquo US Patent
4327417 1982[11] Y-H Kim W-J Kim K-H Song J-W Han and H-N Kim
ldquoModeling of a radar signal for scan patternrdquo in Proceedings ofthe IEEE Military Communications Conference (MILCOM rsquo09)pp 1ndash6 Vancouver Canada October 2009
[12] T H Greer ldquoAutomatic recognition of radar scan typerdquo USPatent 6697007 2004
[13] B Barshan and B Eravci ldquoAutomatic radar antenna scantype recognition in electronic warfarerdquo IEEE Transactions onAerospace and Electronic Systems vol 48 no 4 pp 2908ndash29312012
[14] X-S Wang S-P Xiao D-J Feng and F Zhao Modeling andSimulation of Modern Radar and Electronic Warfare SystemsPublishing House of Electronics Industry Xian China 2010
[15] B R Mahafza and A Z Elsherbeni MATLAB Simulations forRadar Systems Design CRC Press Boca Raton Fla USA 2004
[16] G-X Zhang and X Li ldquoA new recognition system for radaremitter signalsrdquo Kybernetes vol 41 no 9 pp 1351ndash1360 2012
[17] H K Mardia ldquoNew techniques for the deinterleaving ofrepetitive sequencesrdquo IEE proceedings vol 136 no 4 pp 149ndash154 1989
[18] D J Milojevic and B M Popovic ldquoImproved algorithm for thedeinterleaving of radar pulsesrdquo IEE Proceedings F vol 139 no 1pp 98ndash104 1992
[19] D J Nelson ldquoSpecial purpose correlation functions forimproved signal detection and parameter estimationrdquo in Pro-ceedings of the International Conference on Acoustics Speechand Signal Processing (ICASSP rsquo93) pp 73ndash76 1993
[20] P Wei L Qiu and H Wu ldquoThe sorting of radar pulsesignal based on plane transformationrdquo in Proceedings of theInternational Conference on Systems and Informatics (ICSAI rsquo12)pp 326ndash328 May 2012
[21] W-T Lo Y-S Chang R-K Sheu C-C Chiu and S-M YuanldquoCUDT a CUDA based decision tree algorithmrdquoThe ScientificWorld Journal vol 2014 Article ID 745640 12 pages 2014
[22] Q-D Wu B Yan C Zhang L Wang G-B Ning and BYu ldquoDisplacement prediction of tunnel surrounding rock acomparison of support vector machine and artificial neuralnetworkrdquo Mathematical Problems in Engineering vol 2014Article ID 351496 6 pages 2014
[23] B Eravcg Automatic radar antenna scan analysis in electronicwarfare [MS thesis] Department of Electrical and ElectronicsEngineering Bilkent University Ankara Turkey 2010
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
International Journal of Antennas and Propagation 5
64 65 66 67 68 69 7 71 72 730
05
1
Time (s)
Nor
mal
ized
PA
(a) ES
0
05
1
Nor
mal
ized
PA
64 65 66 67 68 69 7 71
Time (s)
(b) MS
Figure 5 Beam sequence
0 50 100 150 200 250 300 350 400minus1
minus05
0
05
1
Index
1st
diffe
renc
e
120 140 160 180minus1
0
1
(a) ES
0 50 100 150 200 250 300minus01
0
01
02
03
Index
1st
diffe
renc
e(b) MS
Figure 6 First-order difference sequence
0 50 100 150 200 250 300 350 400
005
1
Index
minus05minus12
nd d
iffer
ence
(a) ES
0 50 100 150 200 250 300
0005
01015
Index
minus0052nd
diff
eren
ce
(b) MS
Figure 7 Second-order difference sequence
side beam What is different is that the sequence 1198891 shows
gradual change at most points for MS and the maximumvalue is much less than 1
Because most values of sequence 1198891 for ES are near 0
we further calculate the second-order difference sequence
1198892
[119898] = 1198891
[119898 + 1] minus 1198891
[119898] 119898 = 0 1 119873119889
minus 1 (6)
Then theremay be slight variation between 1198892[119898] and 119889
1[119898+
1] for most 119898 for ES The second-order difference sequencesof example sequences in Figure 5 are illustrated in Figure 7
In order to accomplish the recognition we haveattempted to study a number of different features to identifyES but the best results were obtained with the featuredescribed below
By comparing the first-order difference sequences inFigure 6 with the second-order ones in Figure 7 we foundthat there are some notable differences between ES and MSFor ES the first- and second-order difference sequences seemsimilar at most points except the points where main or sidebeams change while for MS the two sequences are quite
differentTherefore we select RC as the feature to distinguishMS and ES
Definition 1 Suppose that discrete signal sequences 1198781(119894) 119894 =
0 1 119873 and 1198782(119895) 119895 = 0 1 119873 are one-dimensional
and nonnegative that is 1198781(119894) ge 0 119878
2(119894) ge 0 119894 = 0 1 119873
RC of 1198781 and 119878
2 is defined as
119862119903
=sum 1198781
(119894) 1198782
(119894)
(radicsum 1198782
1(119894) sdot radicsum 119878
2
2(119894))
(7)
where all points of signal sequences 1198781(119894) and 119878
2(119895) are not
0 Obviously we can get 0 le 119862119903
le 1In order to obtain the RC from the two difference
sequences we set 1198782(119894) = |119889
2(119894)| 119894 = 0 1 119873
119889minus 1 and
1198781 is given by the following formula
1198781
(119894) = max (10038161003816100381610038161198891 (119894 + 1)
1003816100381610038161003816 10038161003816100381610038161198892 (119894)
1003816100381610038161003816) 119894 = 0 1 119873119889
minus 1
(8)
Then the RC can be calculated using formula (7) The valueof 119862119903is large for ES and nearly equal to 1 while it is much less
for MS
6 International Journal of Antennas and Propagation
Features
Cr gt Hr Cr Hr
ES MS
Vd gt H Vd H
1D ES 2D ES
⩽
⩽
Figure 8 The DT structure for recognition of 1D ES
Furthermore in order to differentiate 1D ES from 2D ESwe further process the first-order difference sequence 119889
1
First pick up the elements less than 119867119886in sequence |119889
1|
119867119886is the threshold Hence the picked points present the
variations among a beam position for ES Then compute thevariance of picked elements which is denoted by 119881
119889 Because
there is no beam movement among a beam position for 2DES 119881119889of 2D ES is commonly less than that for 1D ES for
which beam still moves inertially in azimuth
35 Recognition The recognition of 1D ES has two stagesfirst distinguishing ES from MS and then distinguishing 1DES from 2D ES
After extracting the two features we can accomplishthe recognition by utilizing a classifier such as naive Bayesdecision tree (DT) [21] clustering analysis artificial neuralnetworks [22] and support vectormachine [22] Based on therecognition procedure in this paper a DT classifier based onclustering analysis is chosen to recognize 1D ES
A DT classifier is one of the most intuitive and naturalclassifiers and it is fast comprehensible and easy to visualizeA DT consists of two kinds of nodes an internal noderepresents a decision rule and a leaf node shows the resultof a decisionThe DT structure used for recognition of 1D ESis shown in Figure 8
Clustering analysis is the task of grouping a set of objectsin such a way that objects in the same cluster are moresimilar to each other than to those in other clusters It isused in many fields including pattern recognition machinelearning and information retrieval In this paper clusteringis mainly used to determine the threshold of decision rulein DT The Euclidean distance criterion is most commonlyemployed in clustering and the Euclidean distance between1199111
= (11991111
1199111119895
) and 1199112
= (11991121
1199112119895
) of instances isdefined as
119889 (1199111 1199112) = radic
119895
sum
119894=1
(1199111119894
minus 1199112119894
)2
(9)
Since there is only one feature applied in each decisionrule the cluster center can be easily obtained by takingthe mean of train data with every AST For simplicity we
EW receiver
Radar
Beam position variation in
elevation
Azimuth
rotation
NB
NP A
Figure 9 A sketch map of NB and NP for 3D radar with 1D ES
select the median of the cluster centers of two leaf nodesas the threshold in each decision Then 119867
119903and 119867V can be
determined and recognition can be carried out according tothe DT in Figure 8
36 Parameter Extraction This paper mainly considers the1D ES of 3D radar which employs a judicious mix of elec-tronic beam steering orderly in elevation and mechanicallymovement in azimuth After identification of the 1D ES fromMS and 2D ES we need to extract some crucial parametersof 1D ES of 3D radar Besides ASP the key parameters of1D electronic scanning are the beam position number inelevation (defined by NB) and pulse number in a beamposition (defined by NP) which are important to estimatethe beam direction of 3D radar as shown in Figure 9 Forsimplicity bothNB andNP are assumed to be constant in thisstudy The NB and NP are 6 and 4 respectively in Figure 9
The parameter extraction of 1D ES of 3D radar is mainlybased on the beam sequence 119886
119898 extracted in Section 33
From the first-order difference sequence of the sequence119886119898
shown in the subgraph of Figure 6(a) we can see thatthere exist a peak and a trough at two sides of every beamposition which result from the beam position variation inelevation Figure 10 depicts the first difference sequence of thesubgraph and its corresponding PA sequence Consequentlywe can extract NP by determining the interval between thehighest peak and the lowest trough of the beam position withmaximum PA Firstly locate the maximum PA of sequence119886119898
denoted by 119886119898
[119872119887] 0 lt 119872
119886lt 119873119889 Obviously 119886
119898[119872119887]
is equal to 119886[119872119886] which is extracted in Section 33 Then as
for the sequence 1198891 locate the nearby highest peak before
1198891[119872119887] (denoted by 119889
1[119872119901
]) and the nearby lowest troughafter 119889
1[119872119887] (denoted by 119889
1[119872119905]) For the example sequence
shown in Figure 5(b) both 1198891[119872119901
] and 1198891[119872119905] are marked
with red circles in Figure 10(b) Hence the pulse number in abeam position can be calculated by 119873119875 = 119872
119905minus 119872119901
In order to extract NB the steer circle in elevation of1D ES that is the pulse number in an elevation circleshould be determined An example of an elevation circlesequence is marked with red in Figure 10(a) Because thebeam sequences of two adjacent elevation circles are similarin amplitude we can compare the entire sequence 119886
119898 with
reference sequence to estimate the elevation circle intervaland normalized cross-correlation coefficients are applied
International Journal of Antennas and Propagation 7
66 662 664 666 668 670
05
1
Time (s)
Nor
mal
ized
PA
Elevation circlesequenceBeam position
sequence
Referencesequence
(a) Normalized PA
120 130 140 150 160 170 180minus1
minus05
0
05
1
Index
1st
diffe
renc
e
Mt
Mp
(b) 1st difference
Figure 10 An extracted PA sequence and first difference sequence for 1D ES
here Firstly we determine the reference sequence After NPextraction the beam position sequence with maximum PAcan be obtained easily namely 119886
119898[119872119901
+ 1] 119886119898
[119872119905]
marked with blue in Figure 10(a) Then we select the PA dataof the maximum beam position and the adjacent beam posi-tions at its both sides as the reference sequence 119910[119898] 119898 =
0 1 119873119910
minus 1 marked with black in Figure 10(a) Hence119910 = 119886
119898[119872119901
+1minus119873119875] 119886119898
[119872119905+119873119875] and119873
119910= 3times119873119875
Calculate the mean square errors (MSE) of sequence 119910 and119886119898
119890119886119910
[119897] = radic
119873119910minus1
sum
119899=0
(119910 [119899] minus 119886119898
[119899 + 119897])2
119897 = 0 1 119873119889
+ 1 minus 119873119910
(10)
The MSE sequence of the example sequence is shownin Figure 11 from which we can see that in sequence 119890
119886119910
there are several troughs which are located where there aremain beam positions Among these troughs the lowest onewhose value is equal to 0 represents themaximummain beamposition namely the reference sequence 119910 Furthermorethe secondary lowest trough represents the adjacent mainbeam position Let 119873
119898denote the interval of the two troughs
which are marked with red circles in Figure 11 so that NB canbe obtained by 119873119861 = 119873
119898119873119875
4 Simulations
In order to illustrate the proposed algorithm for recognitionof 1D ES several simulations are discussed in this section
41 Recognition At first 60 data sequences are generatedfor training of feature extraction and recognition describedin Sections 34 and 35 20 from 1D ES 20 from 2D ESand the remaining from MS All the ES data are generatedby the simulator designed in Section 2 and the MS data bythe antenna scan pattern simulator in literature [23] Whilegenerating the simulated data we have varied some differentparameters (eg periodweightings angles and so on) so thatthe simulated data resemble the actual data from differentrealistic scenarios as much as possible
0 50 100 150 200 250 300 350 4000
05
1
15
2
25
3
Index
MSE
Figure 11 The MSE sequence
Table 1 Recognition results
Input Classification Recognition rate ()1D ES 2D ES MS
1D ES 88 11 1 882D ES 8 92 0 92MS 4 1 95 95
The features extracted from the simulated data sequenceare depicted in Figure 12 We can see that the feature 119862
119903is
distinguishable Most 119862119903values are equal to 1 for both 1D ES
and 2D ES Although the values of 119862119903are above 07 for MS
they are still less than those for ES The threshold 119867119903is set to
088 based on the train data Moreover the 119881119889value for 1D ES
is generally greater than that for 2D ES and the threshold 119867Vis set to 000031
The number of simulated samples is increased to 100for each AST for recognition Table 1 presents the specificrecognition results The data illustrate that the methodproposed in this paper is effective to distinguish ES fromMS The recognition rates of different ASTs are equal to orabove 88 and the classification accuracy for ES (1D and 2D)even reaches 995 It is suggested to refer to literature [13]for further classification of specific MS type such as circularscan sector scan raster scan and helical scan
8 International Journal of Antennas and Propagation
2 4 6 8 10 12 14 16 18 2006
065
07
075
08
085
09
095
1
105
11
Data index
Cr
1D ES2D ESMS
(a) 119862119903
2 4 6 8 10 12 14 16 18 200
02
04
06
08
1
12
14
16
18
2times10minus3
Data index
Vd
1D ES2D ES
(b) 119881119889
Figure 12 The extracted features
Table 2 Simulation scenarios
Scenario MSlowast Scope Inlowastlowast NP extraction rate () NB extraction rate ()1 Circular Yes 95 952 Sector Yes 94 943 Circular No 94 934 Sector No 89 89lowast
Themechanical scanning type applied by 3D radar in azimuthlowastlowastDoes the EW receiver locate in the elevation scope of 3D radar
42 Parameter Extraction In order to validate the parameterextraction for 1D ES 4 different scenarios are considered inthis simulation which are listed in Table 2 When the EWreceiver does not locate in the elevation scope of 3D radar itcan only receive the signals from the side beams of 3D radarThe simulated samples in which the acquired pulses are notsufficient for parameter extraction are aborted
Unlike estimated parameters (eg ASP) which are avail-able in a tolerance range the main parameters extracted inthis paper (NP and NB) will be invalid if the extracted valuesare incorrect Therefore the extraction accuracy is used toevaluate the performance of parameter extraction here Theextraction result is illustrated in Table 2 It can be observedthat the correct extraction rate of NP is almost equal to thatof NB The reason is that the extraction of NB is based onthe result ofNP extractionThe extraction accuracies are over89 and with slight difference for all 4 scenarios It can bededuced that the parameter extraction is effective even by theside beams signals
5 Conclusions
In this paper we studied the problem of recognition andparameter extraction of 1D ES for 3D radar on which noclear and accessible study is available in the open literatureThe main contribution of this work is making a maidenattempt to resolve the problem and providing an effective
method Firstly an ES pattern simulator to synthesize PAdata is designed Then the methodology for recognition andparameter extraction of 1D ES for 3D radar is explained indetail Finally the algorithm is validated by simulation
Our study will be beneficial to 3D radar identificationfor ELINT and ESM systems Moreover it is also usefulfor estimating the beam direction of 3D radar Our mainfollowing work is to validate the algorithm by real radardata acquired by EW receivers To achieve the purpose someexperiments are going to be designed in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by the National Natural ScienceFoundation of China (no 61201336)
References
[1] J Wisniewski and M Mazur ldquoNaval 3D radar in C bandrdquoin Proceedings of the 14th IEEE International Conference onMicrowaves Radar andWireless Communications (MIKON rsquo02)vol 3 pp 843ndash846 2002
International Journal of Antennas and Propagation 9
[2] F Orsini B di Loreto and A Giacomini ldquo3D X-band tacticalacquisition radar (X-TAR 3D)rdquo in Proceedings of the EuropeanRadar Conference pp 589ndash592 October 2009
[3] D L Adamy EW 102 A Second Course in Electronic WarfareArtech House Boston Mass USA 2004
[4] A Irci A Saranli and B Baykal ldquoStudy on Q-RAM andfeasible directions based methods for resource management inphased array radar systemsrdquo IEEE Transactions on Aerospaceand Electronic Systems vol 46 no 4 pp 1848ndash1864 2010
[5] R G Wiley ELINT The Interception and Analysis of RadarSignals Artech House Boston Mass USA 2006
[6] M Zhu K-C Fu X-Y Huang S-D Wu and W-D Jin ldquoAnovel recognition approach for radar emitter signals based onon-line independent support vector machinesrdquo Advances inComputer Science and Its Applications vol 2 no 3 pp 390ndash3962013
[7] D H A Maithripala and S Jayasuriya ldquoRadar deceptionthrough phantom track generationrdquo in Proceedings of theAmerican Control Conference (ACC rsquo05) pp 4102ndash4106 June2005
[8] B Falk ldquoAntenna PatternGeneratorrdquoUS Patent 3540046 1970[9] A B Evan Jr ldquoDigital antenna pattern generator for radar
simulationrdquo US Patent 4008476 1977[10] T E Zaczek ldquoAntenna scan pattern generatorrdquo US Patent
4327417 1982[11] Y-H Kim W-J Kim K-H Song J-W Han and H-N Kim
ldquoModeling of a radar signal for scan patternrdquo in Proceedings ofthe IEEE Military Communications Conference (MILCOM rsquo09)pp 1ndash6 Vancouver Canada October 2009
[12] T H Greer ldquoAutomatic recognition of radar scan typerdquo USPatent 6697007 2004
[13] B Barshan and B Eravci ldquoAutomatic radar antenna scantype recognition in electronic warfarerdquo IEEE Transactions onAerospace and Electronic Systems vol 48 no 4 pp 2908ndash29312012
[14] X-S Wang S-P Xiao D-J Feng and F Zhao Modeling andSimulation of Modern Radar and Electronic Warfare SystemsPublishing House of Electronics Industry Xian China 2010
[15] B R Mahafza and A Z Elsherbeni MATLAB Simulations forRadar Systems Design CRC Press Boca Raton Fla USA 2004
[16] G-X Zhang and X Li ldquoA new recognition system for radaremitter signalsrdquo Kybernetes vol 41 no 9 pp 1351ndash1360 2012
[17] H K Mardia ldquoNew techniques for the deinterleaving ofrepetitive sequencesrdquo IEE proceedings vol 136 no 4 pp 149ndash154 1989
[18] D J Milojevic and B M Popovic ldquoImproved algorithm for thedeinterleaving of radar pulsesrdquo IEE Proceedings F vol 139 no 1pp 98ndash104 1992
[19] D J Nelson ldquoSpecial purpose correlation functions forimproved signal detection and parameter estimationrdquo in Pro-ceedings of the International Conference on Acoustics Speechand Signal Processing (ICASSP rsquo93) pp 73ndash76 1993
[20] P Wei L Qiu and H Wu ldquoThe sorting of radar pulsesignal based on plane transformationrdquo in Proceedings of theInternational Conference on Systems and Informatics (ICSAI rsquo12)pp 326ndash328 May 2012
[21] W-T Lo Y-S Chang R-K Sheu C-C Chiu and S-M YuanldquoCUDT a CUDA based decision tree algorithmrdquoThe ScientificWorld Journal vol 2014 Article ID 745640 12 pages 2014
[22] Q-D Wu B Yan C Zhang L Wang G-B Ning and BYu ldquoDisplacement prediction of tunnel surrounding rock acomparison of support vector machine and artificial neuralnetworkrdquo Mathematical Problems in Engineering vol 2014Article ID 351496 6 pages 2014
[23] B Eravcg Automatic radar antenna scan analysis in electronicwarfare [MS thesis] Department of Electrical and ElectronicsEngineering Bilkent University Ankara Turkey 2010
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
6 International Journal of Antennas and Propagation
Features
Cr gt Hr Cr Hr
ES MS
Vd gt H Vd H
1D ES 2D ES
⩽
⩽
Figure 8 The DT structure for recognition of 1D ES
Furthermore in order to differentiate 1D ES from 2D ESwe further process the first-order difference sequence 119889
1
First pick up the elements less than 119867119886in sequence |119889
1|
119867119886is the threshold Hence the picked points present the
variations among a beam position for ES Then compute thevariance of picked elements which is denoted by 119881
119889 Because
there is no beam movement among a beam position for 2DES 119881119889of 2D ES is commonly less than that for 1D ES for
which beam still moves inertially in azimuth
35 Recognition The recognition of 1D ES has two stagesfirst distinguishing ES from MS and then distinguishing 1DES from 2D ES
After extracting the two features we can accomplishthe recognition by utilizing a classifier such as naive Bayesdecision tree (DT) [21] clustering analysis artificial neuralnetworks [22] and support vectormachine [22] Based on therecognition procedure in this paper a DT classifier based onclustering analysis is chosen to recognize 1D ES
A DT classifier is one of the most intuitive and naturalclassifiers and it is fast comprehensible and easy to visualizeA DT consists of two kinds of nodes an internal noderepresents a decision rule and a leaf node shows the resultof a decisionThe DT structure used for recognition of 1D ESis shown in Figure 8
Clustering analysis is the task of grouping a set of objectsin such a way that objects in the same cluster are moresimilar to each other than to those in other clusters It isused in many fields including pattern recognition machinelearning and information retrieval In this paper clusteringis mainly used to determine the threshold of decision rulein DT The Euclidean distance criterion is most commonlyemployed in clustering and the Euclidean distance between1199111
= (11991111
1199111119895
) and 1199112
= (11991121
1199112119895
) of instances isdefined as
119889 (1199111 1199112) = radic
119895
sum
119894=1
(1199111119894
minus 1199112119894
)2
(9)
Since there is only one feature applied in each decisionrule the cluster center can be easily obtained by takingthe mean of train data with every AST For simplicity we
EW receiver
Radar
Beam position variation in
elevation
Azimuth
rotation
NB
NP A
Figure 9 A sketch map of NB and NP for 3D radar with 1D ES
select the median of the cluster centers of two leaf nodesas the threshold in each decision Then 119867
119903and 119867V can be
determined and recognition can be carried out according tothe DT in Figure 8
36 Parameter Extraction This paper mainly considers the1D ES of 3D radar which employs a judicious mix of elec-tronic beam steering orderly in elevation and mechanicallymovement in azimuth After identification of the 1D ES fromMS and 2D ES we need to extract some crucial parametersof 1D ES of 3D radar Besides ASP the key parameters of1D electronic scanning are the beam position number inelevation (defined by NB) and pulse number in a beamposition (defined by NP) which are important to estimatethe beam direction of 3D radar as shown in Figure 9 Forsimplicity bothNB andNP are assumed to be constant in thisstudy The NB and NP are 6 and 4 respectively in Figure 9
The parameter extraction of 1D ES of 3D radar is mainlybased on the beam sequence 119886
119898 extracted in Section 33
From the first-order difference sequence of the sequence119886119898
shown in the subgraph of Figure 6(a) we can see thatthere exist a peak and a trough at two sides of every beamposition which result from the beam position variation inelevation Figure 10 depicts the first difference sequence of thesubgraph and its corresponding PA sequence Consequentlywe can extract NP by determining the interval between thehighest peak and the lowest trough of the beam position withmaximum PA Firstly locate the maximum PA of sequence119886119898
denoted by 119886119898
[119872119887] 0 lt 119872
119886lt 119873119889 Obviously 119886
119898[119872119887]
is equal to 119886[119872119886] which is extracted in Section 33 Then as
for the sequence 1198891 locate the nearby highest peak before
1198891[119872119887] (denoted by 119889
1[119872119901
]) and the nearby lowest troughafter 119889
1[119872119887] (denoted by 119889
1[119872119905]) For the example sequence
shown in Figure 5(b) both 1198891[119872119901
] and 1198891[119872119905] are marked
with red circles in Figure 10(b) Hence the pulse number in abeam position can be calculated by 119873119875 = 119872
119905minus 119872119901
In order to extract NB the steer circle in elevation of1D ES that is the pulse number in an elevation circleshould be determined An example of an elevation circlesequence is marked with red in Figure 10(a) Because thebeam sequences of two adjacent elevation circles are similarin amplitude we can compare the entire sequence 119886
119898 with
reference sequence to estimate the elevation circle intervaland normalized cross-correlation coefficients are applied
International Journal of Antennas and Propagation 7
66 662 664 666 668 670
05
1
Time (s)
Nor
mal
ized
PA
Elevation circlesequenceBeam position
sequence
Referencesequence
(a) Normalized PA
120 130 140 150 160 170 180minus1
minus05
0
05
1
Index
1st
diffe
renc
e
Mt
Mp
(b) 1st difference
Figure 10 An extracted PA sequence and first difference sequence for 1D ES
here Firstly we determine the reference sequence After NPextraction the beam position sequence with maximum PAcan be obtained easily namely 119886
119898[119872119901
+ 1] 119886119898
[119872119905]
marked with blue in Figure 10(a) Then we select the PA dataof the maximum beam position and the adjacent beam posi-tions at its both sides as the reference sequence 119910[119898] 119898 =
0 1 119873119910
minus 1 marked with black in Figure 10(a) Hence119910 = 119886
119898[119872119901
+1minus119873119875] 119886119898
[119872119905+119873119875] and119873
119910= 3times119873119875
Calculate the mean square errors (MSE) of sequence 119910 and119886119898
119890119886119910
[119897] = radic
119873119910minus1
sum
119899=0
(119910 [119899] minus 119886119898
[119899 + 119897])2
119897 = 0 1 119873119889
+ 1 minus 119873119910
(10)
The MSE sequence of the example sequence is shownin Figure 11 from which we can see that in sequence 119890
119886119910
there are several troughs which are located where there aremain beam positions Among these troughs the lowest onewhose value is equal to 0 represents themaximummain beamposition namely the reference sequence 119910 Furthermorethe secondary lowest trough represents the adjacent mainbeam position Let 119873
119898denote the interval of the two troughs
which are marked with red circles in Figure 11 so that NB canbe obtained by 119873119861 = 119873
119898119873119875
4 Simulations
In order to illustrate the proposed algorithm for recognitionof 1D ES several simulations are discussed in this section
41 Recognition At first 60 data sequences are generatedfor training of feature extraction and recognition describedin Sections 34 and 35 20 from 1D ES 20 from 2D ESand the remaining from MS All the ES data are generatedby the simulator designed in Section 2 and the MS data bythe antenna scan pattern simulator in literature [23] Whilegenerating the simulated data we have varied some differentparameters (eg periodweightings angles and so on) so thatthe simulated data resemble the actual data from differentrealistic scenarios as much as possible
0 50 100 150 200 250 300 350 4000
05
1
15
2
25
3
Index
MSE
Figure 11 The MSE sequence
Table 1 Recognition results
Input Classification Recognition rate ()1D ES 2D ES MS
1D ES 88 11 1 882D ES 8 92 0 92MS 4 1 95 95
The features extracted from the simulated data sequenceare depicted in Figure 12 We can see that the feature 119862
119903is
distinguishable Most 119862119903values are equal to 1 for both 1D ES
and 2D ES Although the values of 119862119903are above 07 for MS
they are still less than those for ES The threshold 119867119903is set to
088 based on the train data Moreover the 119881119889value for 1D ES
is generally greater than that for 2D ES and the threshold 119867Vis set to 000031
The number of simulated samples is increased to 100for each AST for recognition Table 1 presents the specificrecognition results The data illustrate that the methodproposed in this paper is effective to distinguish ES fromMS The recognition rates of different ASTs are equal to orabove 88 and the classification accuracy for ES (1D and 2D)even reaches 995 It is suggested to refer to literature [13]for further classification of specific MS type such as circularscan sector scan raster scan and helical scan
8 International Journal of Antennas and Propagation
2 4 6 8 10 12 14 16 18 2006
065
07
075
08
085
09
095
1
105
11
Data index
Cr
1D ES2D ESMS
(a) 119862119903
2 4 6 8 10 12 14 16 18 200
02
04
06
08
1
12
14
16
18
2times10minus3
Data index
Vd
1D ES2D ES
(b) 119881119889
Figure 12 The extracted features
Table 2 Simulation scenarios
Scenario MSlowast Scope Inlowastlowast NP extraction rate () NB extraction rate ()1 Circular Yes 95 952 Sector Yes 94 943 Circular No 94 934 Sector No 89 89lowast
Themechanical scanning type applied by 3D radar in azimuthlowastlowastDoes the EW receiver locate in the elevation scope of 3D radar
42 Parameter Extraction In order to validate the parameterextraction for 1D ES 4 different scenarios are considered inthis simulation which are listed in Table 2 When the EWreceiver does not locate in the elevation scope of 3D radar itcan only receive the signals from the side beams of 3D radarThe simulated samples in which the acquired pulses are notsufficient for parameter extraction are aborted
Unlike estimated parameters (eg ASP) which are avail-able in a tolerance range the main parameters extracted inthis paper (NP and NB) will be invalid if the extracted valuesare incorrect Therefore the extraction accuracy is used toevaluate the performance of parameter extraction here Theextraction result is illustrated in Table 2 It can be observedthat the correct extraction rate of NP is almost equal to thatof NB The reason is that the extraction of NB is based onthe result ofNP extractionThe extraction accuracies are over89 and with slight difference for all 4 scenarios It can bededuced that the parameter extraction is effective even by theside beams signals
5 Conclusions
In this paper we studied the problem of recognition andparameter extraction of 1D ES for 3D radar on which noclear and accessible study is available in the open literatureThe main contribution of this work is making a maidenattempt to resolve the problem and providing an effective
method Firstly an ES pattern simulator to synthesize PAdata is designed Then the methodology for recognition andparameter extraction of 1D ES for 3D radar is explained indetail Finally the algorithm is validated by simulation
Our study will be beneficial to 3D radar identificationfor ELINT and ESM systems Moreover it is also usefulfor estimating the beam direction of 3D radar Our mainfollowing work is to validate the algorithm by real radardata acquired by EW receivers To achieve the purpose someexperiments are going to be designed in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by the National Natural ScienceFoundation of China (no 61201336)
References
[1] J Wisniewski and M Mazur ldquoNaval 3D radar in C bandrdquoin Proceedings of the 14th IEEE International Conference onMicrowaves Radar andWireless Communications (MIKON rsquo02)vol 3 pp 843ndash846 2002
International Journal of Antennas and Propagation 9
[2] F Orsini B di Loreto and A Giacomini ldquo3D X-band tacticalacquisition radar (X-TAR 3D)rdquo in Proceedings of the EuropeanRadar Conference pp 589ndash592 October 2009
[3] D L Adamy EW 102 A Second Course in Electronic WarfareArtech House Boston Mass USA 2004
[4] A Irci A Saranli and B Baykal ldquoStudy on Q-RAM andfeasible directions based methods for resource management inphased array radar systemsrdquo IEEE Transactions on Aerospaceand Electronic Systems vol 46 no 4 pp 1848ndash1864 2010
[5] R G Wiley ELINT The Interception and Analysis of RadarSignals Artech House Boston Mass USA 2006
[6] M Zhu K-C Fu X-Y Huang S-D Wu and W-D Jin ldquoAnovel recognition approach for radar emitter signals based onon-line independent support vector machinesrdquo Advances inComputer Science and Its Applications vol 2 no 3 pp 390ndash3962013
[7] D H A Maithripala and S Jayasuriya ldquoRadar deceptionthrough phantom track generationrdquo in Proceedings of theAmerican Control Conference (ACC rsquo05) pp 4102ndash4106 June2005
[8] B Falk ldquoAntenna PatternGeneratorrdquoUS Patent 3540046 1970[9] A B Evan Jr ldquoDigital antenna pattern generator for radar
simulationrdquo US Patent 4008476 1977[10] T E Zaczek ldquoAntenna scan pattern generatorrdquo US Patent
4327417 1982[11] Y-H Kim W-J Kim K-H Song J-W Han and H-N Kim
ldquoModeling of a radar signal for scan patternrdquo in Proceedings ofthe IEEE Military Communications Conference (MILCOM rsquo09)pp 1ndash6 Vancouver Canada October 2009
[12] T H Greer ldquoAutomatic recognition of radar scan typerdquo USPatent 6697007 2004
[13] B Barshan and B Eravci ldquoAutomatic radar antenna scantype recognition in electronic warfarerdquo IEEE Transactions onAerospace and Electronic Systems vol 48 no 4 pp 2908ndash29312012
[14] X-S Wang S-P Xiao D-J Feng and F Zhao Modeling andSimulation of Modern Radar and Electronic Warfare SystemsPublishing House of Electronics Industry Xian China 2010
[15] B R Mahafza and A Z Elsherbeni MATLAB Simulations forRadar Systems Design CRC Press Boca Raton Fla USA 2004
[16] G-X Zhang and X Li ldquoA new recognition system for radaremitter signalsrdquo Kybernetes vol 41 no 9 pp 1351ndash1360 2012
[17] H K Mardia ldquoNew techniques for the deinterleaving ofrepetitive sequencesrdquo IEE proceedings vol 136 no 4 pp 149ndash154 1989
[18] D J Milojevic and B M Popovic ldquoImproved algorithm for thedeinterleaving of radar pulsesrdquo IEE Proceedings F vol 139 no 1pp 98ndash104 1992
[19] D J Nelson ldquoSpecial purpose correlation functions forimproved signal detection and parameter estimationrdquo in Pro-ceedings of the International Conference on Acoustics Speechand Signal Processing (ICASSP rsquo93) pp 73ndash76 1993
[20] P Wei L Qiu and H Wu ldquoThe sorting of radar pulsesignal based on plane transformationrdquo in Proceedings of theInternational Conference on Systems and Informatics (ICSAI rsquo12)pp 326ndash328 May 2012
[21] W-T Lo Y-S Chang R-K Sheu C-C Chiu and S-M YuanldquoCUDT a CUDA based decision tree algorithmrdquoThe ScientificWorld Journal vol 2014 Article ID 745640 12 pages 2014
[22] Q-D Wu B Yan C Zhang L Wang G-B Ning and BYu ldquoDisplacement prediction of tunnel surrounding rock acomparison of support vector machine and artificial neuralnetworkrdquo Mathematical Problems in Engineering vol 2014Article ID 351496 6 pages 2014
[23] B Eravcg Automatic radar antenna scan analysis in electronicwarfare [MS thesis] Department of Electrical and ElectronicsEngineering Bilkent University Ankara Turkey 2010
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
International Journal of Antennas and Propagation 7
66 662 664 666 668 670
05
1
Time (s)
Nor
mal
ized
PA
Elevation circlesequenceBeam position
sequence
Referencesequence
(a) Normalized PA
120 130 140 150 160 170 180minus1
minus05
0
05
1
Index
1st
diffe
renc
e
Mt
Mp
(b) 1st difference
Figure 10 An extracted PA sequence and first difference sequence for 1D ES
here Firstly we determine the reference sequence After NPextraction the beam position sequence with maximum PAcan be obtained easily namely 119886
119898[119872119901
+ 1] 119886119898
[119872119905]
marked with blue in Figure 10(a) Then we select the PA dataof the maximum beam position and the adjacent beam posi-tions at its both sides as the reference sequence 119910[119898] 119898 =
0 1 119873119910
minus 1 marked with black in Figure 10(a) Hence119910 = 119886
119898[119872119901
+1minus119873119875] 119886119898
[119872119905+119873119875] and119873
119910= 3times119873119875
Calculate the mean square errors (MSE) of sequence 119910 and119886119898
119890119886119910
[119897] = radic
119873119910minus1
sum
119899=0
(119910 [119899] minus 119886119898
[119899 + 119897])2
119897 = 0 1 119873119889
+ 1 minus 119873119910
(10)
The MSE sequence of the example sequence is shownin Figure 11 from which we can see that in sequence 119890
119886119910
there are several troughs which are located where there aremain beam positions Among these troughs the lowest onewhose value is equal to 0 represents themaximummain beamposition namely the reference sequence 119910 Furthermorethe secondary lowest trough represents the adjacent mainbeam position Let 119873
119898denote the interval of the two troughs
which are marked with red circles in Figure 11 so that NB canbe obtained by 119873119861 = 119873
119898119873119875
4 Simulations
In order to illustrate the proposed algorithm for recognitionof 1D ES several simulations are discussed in this section
41 Recognition At first 60 data sequences are generatedfor training of feature extraction and recognition describedin Sections 34 and 35 20 from 1D ES 20 from 2D ESand the remaining from MS All the ES data are generatedby the simulator designed in Section 2 and the MS data bythe antenna scan pattern simulator in literature [23] Whilegenerating the simulated data we have varied some differentparameters (eg periodweightings angles and so on) so thatthe simulated data resemble the actual data from differentrealistic scenarios as much as possible
0 50 100 150 200 250 300 350 4000
05
1
15
2
25
3
Index
MSE
Figure 11 The MSE sequence
Table 1 Recognition results
Input Classification Recognition rate ()1D ES 2D ES MS
1D ES 88 11 1 882D ES 8 92 0 92MS 4 1 95 95
The features extracted from the simulated data sequenceare depicted in Figure 12 We can see that the feature 119862
119903is
distinguishable Most 119862119903values are equal to 1 for both 1D ES
and 2D ES Although the values of 119862119903are above 07 for MS
they are still less than those for ES The threshold 119867119903is set to
088 based on the train data Moreover the 119881119889value for 1D ES
is generally greater than that for 2D ES and the threshold 119867Vis set to 000031
The number of simulated samples is increased to 100for each AST for recognition Table 1 presents the specificrecognition results The data illustrate that the methodproposed in this paper is effective to distinguish ES fromMS The recognition rates of different ASTs are equal to orabove 88 and the classification accuracy for ES (1D and 2D)even reaches 995 It is suggested to refer to literature [13]for further classification of specific MS type such as circularscan sector scan raster scan and helical scan
8 International Journal of Antennas and Propagation
2 4 6 8 10 12 14 16 18 2006
065
07
075
08
085
09
095
1
105
11
Data index
Cr
1D ES2D ESMS
(a) 119862119903
2 4 6 8 10 12 14 16 18 200
02
04
06
08
1
12
14
16
18
2times10minus3
Data index
Vd
1D ES2D ES
(b) 119881119889
Figure 12 The extracted features
Table 2 Simulation scenarios
Scenario MSlowast Scope Inlowastlowast NP extraction rate () NB extraction rate ()1 Circular Yes 95 952 Sector Yes 94 943 Circular No 94 934 Sector No 89 89lowast
Themechanical scanning type applied by 3D radar in azimuthlowastlowastDoes the EW receiver locate in the elevation scope of 3D radar
42 Parameter Extraction In order to validate the parameterextraction for 1D ES 4 different scenarios are considered inthis simulation which are listed in Table 2 When the EWreceiver does not locate in the elevation scope of 3D radar itcan only receive the signals from the side beams of 3D radarThe simulated samples in which the acquired pulses are notsufficient for parameter extraction are aborted
Unlike estimated parameters (eg ASP) which are avail-able in a tolerance range the main parameters extracted inthis paper (NP and NB) will be invalid if the extracted valuesare incorrect Therefore the extraction accuracy is used toevaluate the performance of parameter extraction here Theextraction result is illustrated in Table 2 It can be observedthat the correct extraction rate of NP is almost equal to thatof NB The reason is that the extraction of NB is based onthe result ofNP extractionThe extraction accuracies are over89 and with slight difference for all 4 scenarios It can bededuced that the parameter extraction is effective even by theside beams signals
5 Conclusions
In this paper we studied the problem of recognition andparameter extraction of 1D ES for 3D radar on which noclear and accessible study is available in the open literatureThe main contribution of this work is making a maidenattempt to resolve the problem and providing an effective
method Firstly an ES pattern simulator to synthesize PAdata is designed Then the methodology for recognition andparameter extraction of 1D ES for 3D radar is explained indetail Finally the algorithm is validated by simulation
Our study will be beneficial to 3D radar identificationfor ELINT and ESM systems Moreover it is also usefulfor estimating the beam direction of 3D radar Our mainfollowing work is to validate the algorithm by real radardata acquired by EW receivers To achieve the purpose someexperiments are going to be designed in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by the National Natural ScienceFoundation of China (no 61201336)
References
[1] J Wisniewski and M Mazur ldquoNaval 3D radar in C bandrdquoin Proceedings of the 14th IEEE International Conference onMicrowaves Radar andWireless Communications (MIKON rsquo02)vol 3 pp 843ndash846 2002
International Journal of Antennas and Propagation 9
[2] F Orsini B di Loreto and A Giacomini ldquo3D X-band tacticalacquisition radar (X-TAR 3D)rdquo in Proceedings of the EuropeanRadar Conference pp 589ndash592 October 2009
[3] D L Adamy EW 102 A Second Course in Electronic WarfareArtech House Boston Mass USA 2004
[4] A Irci A Saranli and B Baykal ldquoStudy on Q-RAM andfeasible directions based methods for resource management inphased array radar systemsrdquo IEEE Transactions on Aerospaceand Electronic Systems vol 46 no 4 pp 1848ndash1864 2010
[5] R G Wiley ELINT The Interception and Analysis of RadarSignals Artech House Boston Mass USA 2006
[6] M Zhu K-C Fu X-Y Huang S-D Wu and W-D Jin ldquoAnovel recognition approach for radar emitter signals based onon-line independent support vector machinesrdquo Advances inComputer Science and Its Applications vol 2 no 3 pp 390ndash3962013
[7] D H A Maithripala and S Jayasuriya ldquoRadar deceptionthrough phantom track generationrdquo in Proceedings of theAmerican Control Conference (ACC rsquo05) pp 4102ndash4106 June2005
[8] B Falk ldquoAntenna PatternGeneratorrdquoUS Patent 3540046 1970[9] A B Evan Jr ldquoDigital antenna pattern generator for radar
simulationrdquo US Patent 4008476 1977[10] T E Zaczek ldquoAntenna scan pattern generatorrdquo US Patent
4327417 1982[11] Y-H Kim W-J Kim K-H Song J-W Han and H-N Kim
ldquoModeling of a radar signal for scan patternrdquo in Proceedings ofthe IEEE Military Communications Conference (MILCOM rsquo09)pp 1ndash6 Vancouver Canada October 2009
[12] T H Greer ldquoAutomatic recognition of radar scan typerdquo USPatent 6697007 2004
[13] B Barshan and B Eravci ldquoAutomatic radar antenna scantype recognition in electronic warfarerdquo IEEE Transactions onAerospace and Electronic Systems vol 48 no 4 pp 2908ndash29312012
[14] X-S Wang S-P Xiao D-J Feng and F Zhao Modeling andSimulation of Modern Radar and Electronic Warfare SystemsPublishing House of Electronics Industry Xian China 2010
[15] B R Mahafza and A Z Elsherbeni MATLAB Simulations forRadar Systems Design CRC Press Boca Raton Fla USA 2004
[16] G-X Zhang and X Li ldquoA new recognition system for radaremitter signalsrdquo Kybernetes vol 41 no 9 pp 1351ndash1360 2012
[17] H K Mardia ldquoNew techniques for the deinterleaving ofrepetitive sequencesrdquo IEE proceedings vol 136 no 4 pp 149ndash154 1989
[18] D J Milojevic and B M Popovic ldquoImproved algorithm for thedeinterleaving of radar pulsesrdquo IEE Proceedings F vol 139 no 1pp 98ndash104 1992
[19] D J Nelson ldquoSpecial purpose correlation functions forimproved signal detection and parameter estimationrdquo in Pro-ceedings of the International Conference on Acoustics Speechand Signal Processing (ICASSP rsquo93) pp 73ndash76 1993
[20] P Wei L Qiu and H Wu ldquoThe sorting of radar pulsesignal based on plane transformationrdquo in Proceedings of theInternational Conference on Systems and Informatics (ICSAI rsquo12)pp 326ndash328 May 2012
[21] W-T Lo Y-S Chang R-K Sheu C-C Chiu and S-M YuanldquoCUDT a CUDA based decision tree algorithmrdquoThe ScientificWorld Journal vol 2014 Article ID 745640 12 pages 2014
[22] Q-D Wu B Yan C Zhang L Wang G-B Ning and BYu ldquoDisplacement prediction of tunnel surrounding rock acomparison of support vector machine and artificial neuralnetworkrdquo Mathematical Problems in Engineering vol 2014Article ID 351496 6 pages 2014
[23] B Eravcg Automatic radar antenna scan analysis in electronicwarfare [MS thesis] Department of Electrical and ElectronicsEngineering Bilkent University Ankara Turkey 2010
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
8 International Journal of Antennas and Propagation
2 4 6 8 10 12 14 16 18 2006
065
07
075
08
085
09
095
1
105
11
Data index
Cr
1D ES2D ESMS
(a) 119862119903
2 4 6 8 10 12 14 16 18 200
02
04
06
08
1
12
14
16
18
2times10minus3
Data index
Vd
1D ES2D ES
(b) 119881119889
Figure 12 The extracted features
Table 2 Simulation scenarios
Scenario MSlowast Scope Inlowastlowast NP extraction rate () NB extraction rate ()1 Circular Yes 95 952 Sector Yes 94 943 Circular No 94 934 Sector No 89 89lowast
Themechanical scanning type applied by 3D radar in azimuthlowastlowastDoes the EW receiver locate in the elevation scope of 3D radar
42 Parameter Extraction In order to validate the parameterextraction for 1D ES 4 different scenarios are considered inthis simulation which are listed in Table 2 When the EWreceiver does not locate in the elevation scope of 3D radar itcan only receive the signals from the side beams of 3D radarThe simulated samples in which the acquired pulses are notsufficient for parameter extraction are aborted
Unlike estimated parameters (eg ASP) which are avail-able in a tolerance range the main parameters extracted inthis paper (NP and NB) will be invalid if the extracted valuesare incorrect Therefore the extraction accuracy is used toevaluate the performance of parameter extraction here Theextraction result is illustrated in Table 2 It can be observedthat the correct extraction rate of NP is almost equal to thatof NB The reason is that the extraction of NB is based onthe result ofNP extractionThe extraction accuracies are over89 and with slight difference for all 4 scenarios It can bededuced that the parameter extraction is effective even by theside beams signals
5 Conclusions
In this paper we studied the problem of recognition andparameter extraction of 1D ES for 3D radar on which noclear and accessible study is available in the open literatureThe main contribution of this work is making a maidenattempt to resolve the problem and providing an effective
method Firstly an ES pattern simulator to synthesize PAdata is designed Then the methodology for recognition andparameter extraction of 1D ES for 3D radar is explained indetail Finally the algorithm is validated by simulation
Our study will be beneficial to 3D radar identificationfor ELINT and ESM systems Moreover it is also usefulfor estimating the beam direction of 3D radar Our mainfollowing work is to validate the algorithm by real radardata acquired by EW receivers To achieve the purpose someexperiments are going to be designed in the future
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgment
This work is supported by the National Natural ScienceFoundation of China (no 61201336)
References
[1] J Wisniewski and M Mazur ldquoNaval 3D radar in C bandrdquoin Proceedings of the 14th IEEE International Conference onMicrowaves Radar andWireless Communications (MIKON rsquo02)vol 3 pp 843ndash846 2002
International Journal of Antennas and Propagation 9
[2] F Orsini B di Loreto and A Giacomini ldquo3D X-band tacticalacquisition radar (X-TAR 3D)rdquo in Proceedings of the EuropeanRadar Conference pp 589ndash592 October 2009
[3] D L Adamy EW 102 A Second Course in Electronic WarfareArtech House Boston Mass USA 2004
[4] A Irci A Saranli and B Baykal ldquoStudy on Q-RAM andfeasible directions based methods for resource management inphased array radar systemsrdquo IEEE Transactions on Aerospaceand Electronic Systems vol 46 no 4 pp 1848ndash1864 2010
[5] R G Wiley ELINT The Interception and Analysis of RadarSignals Artech House Boston Mass USA 2006
[6] M Zhu K-C Fu X-Y Huang S-D Wu and W-D Jin ldquoAnovel recognition approach for radar emitter signals based onon-line independent support vector machinesrdquo Advances inComputer Science and Its Applications vol 2 no 3 pp 390ndash3962013
[7] D H A Maithripala and S Jayasuriya ldquoRadar deceptionthrough phantom track generationrdquo in Proceedings of theAmerican Control Conference (ACC rsquo05) pp 4102ndash4106 June2005
[8] B Falk ldquoAntenna PatternGeneratorrdquoUS Patent 3540046 1970[9] A B Evan Jr ldquoDigital antenna pattern generator for radar
simulationrdquo US Patent 4008476 1977[10] T E Zaczek ldquoAntenna scan pattern generatorrdquo US Patent
4327417 1982[11] Y-H Kim W-J Kim K-H Song J-W Han and H-N Kim
ldquoModeling of a radar signal for scan patternrdquo in Proceedings ofthe IEEE Military Communications Conference (MILCOM rsquo09)pp 1ndash6 Vancouver Canada October 2009
[12] T H Greer ldquoAutomatic recognition of radar scan typerdquo USPatent 6697007 2004
[13] B Barshan and B Eravci ldquoAutomatic radar antenna scantype recognition in electronic warfarerdquo IEEE Transactions onAerospace and Electronic Systems vol 48 no 4 pp 2908ndash29312012
[14] X-S Wang S-P Xiao D-J Feng and F Zhao Modeling andSimulation of Modern Radar and Electronic Warfare SystemsPublishing House of Electronics Industry Xian China 2010
[15] B R Mahafza and A Z Elsherbeni MATLAB Simulations forRadar Systems Design CRC Press Boca Raton Fla USA 2004
[16] G-X Zhang and X Li ldquoA new recognition system for radaremitter signalsrdquo Kybernetes vol 41 no 9 pp 1351ndash1360 2012
[17] H K Mardia ldquoNew techniques for the deinterleaving ofrepetitive sequencesrdquo IEE proceedings vol 136 no 4 pp 149ndash154 1989
[18] D J Milojevic and B M Popovic ldquoImproved algorithm for thedeinterleaving of radar pulsesrdquo IEE Proceedings F vol 139 no 1pp 98ndash104 1992
[19] D J Nelson ldquoSpecial purpose correlation functions forimproved signal detection and parameter estimationrdquo in Pro-ceedings of the International Conference on Acoustics Speechand Signal Processing (ICASSP rsquo93) pp 73ndash76 1993
[20] P Wei L Qiu and H Wu ldquoThe sorting of radar pulsesignal based on plane transformationrdquo in Proceedings of theInternational Conference on Systems and Informatics (ICSAI rsquo12)pp 326ndash328 May 2012
[21] W-T Lo Y-S Chang R-K Sheu C-C Chiu and S-M YuanldquoCUDT a CUDA based decision tree algorithmrdquoThe ScientificWorld Journal vol 2014 Article ID 745640 12 pages 2014
[22] Q-D Wu B Yan C Zhang L Wang G-B Ning and BYu ldquoDisplacement prediction of tunnel surrounding rock acomparison of support vector machine and artificial neuralnetworkrdquo Mathematical Problems in Engineering vol 2014Article ID 351496 6 pages 2014
[23] B Eravcg Automatic radar antenna scan analysis in electronicwarfare [MS thesis] Department of Electrical and ElectronicsEngineering Bilkent University Ankara Turkey 2010
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
International Journal of Antennas and Propagation 9
[2] F Orsini B di Loreto and A Giacomini ldquo3D X-band tacticalacquisition radar (X-TAR 3D)rdquo in Proceedings of the EuropeanRadar Conference pp 589ndash592 October 2009
[3] D L Adamy EW 102 A Second Course in Electronic WarfareArtech House Boston Mass USA 2004
[4] A Irci A Saranli and B Baykal ldquoStudy on Q-RAM andfeasible directions based methods for resource management inphased array radar systemsrdquo IEEE Transactions on Aerospaceand Electronic Systems vol 46 no 4 pp 1848ndash1864 2010
[5] R G Wiley ELINT The Interception and Analysis of RadarSignals Artech House Boston Mass USA 2006
[6] M Zhu K-C Fu X-Y Huang S-D Wu and W-D Jin ldquoAnovel recognition approach for radar emitter signals based onon-line independent support vector machinesrdquo Advances inComputer Science and Its Applications vol 2 no 3 pp 390ndash3962013
[7] D H A Maithripala and S Jayasuriya ldquoRadar deceptionthrough phantom track generationrdquo in Proceedings of theAmerican Control Conference (ACC rsquo05) pp 4102ndash4106 June2005
[8] B Falk ldquoAntenna PatternGeneratorrdquoUS Patent 3540046 1970[9] A B Evan Jr ldquoDigital antenna pattern generator for radar
simulationrdquo US Patent 4008476 1977[10] T E Zaczek ldquoAntenna scan pattern generatorrdquo US Patent
4327417 1982[11] Y-H Kim W-J Kim K-H Song J-W Han and H-N Kim
ldquoModeling of a radar signal for scan patternrdquo in Proceedings ofthe IEEE Military Communications Conference (MILCOM rsquo09)pp 1ndash6 Vancouver Canada October 2009
[12] T H Greer ldquoAutomatic recognition of radar scan typerdquo USPatent 6697007 2004
[13] B Barshan and B Eravci ldquoAutomatic radar antenna scantype recognition in electronic warfarerdquo IEEE Transactions onAerospace and Electronic Systems vol 48 no 4 pp 2908ndash29312012
[14] X-S Wang S-P Xiao D-J Feng and F Zhao Modeling andSimulation of Modern Radar and Electronic Warfare SystemsPublishing House of Electronics Industry Xian China 2010
[15] B R Mahafza and A Z Elsherbeni MATLAB Simulations forRadar Systems Design CRC Press Boca Raton Fla USA 2004
[16] G-X Zhang and X Li ldquoA new recognition system for radaremitter signalsrdquo Kybernetes vol 41 no 9 pp 1351ndash1360 2012
[17] H K Mardia ldquoNew techniques for the deinterleaving ofrepetitive sequencesrdquo IEE proceedings vol 136 no 4 pp 149ndash154 1989
[18] D J Milojevic and B M Popovic ldquoImproved algorithm for thedeinterleaving of radar pulsesrdquo IEE Proceedings F vol 139 no 1pp 98ndash104 1992
[19] D J Nelson ldquoSpecial purpose correlation functions forimproved signal detection and parameter estimationrdquo in Pro-ceedings of the International Conference on Acoustics Speechand Signal Processing (ICASSP rsquo93) pp 73ndash76 1993
[20] P Wei L Qiu and H Wu ldquoThe sorting of radar pulsesignal based on plane transformationrdquo in Proceedings of theInternational Conference on Systems and Informatics (ICSAI rsquo12)pp 326ndash328 May 2012
[21] W-T Lo Y-S Chang R-K Sheu C-C Chiu and S-M YuanldquoCUDT a CUDA based decision tree algorithmrdquoThe ScientificWorld Journal vol 2014 Article ID 745640 12 pages 2014
[22] Q-D Wu B Yan C Zhang L Wang G-B Ning and BYu ldquoDisplacement prediction of tunnel surrounding rock acomparison of support vector machine and artificial neuralnetworkrdquo Mathematical Problems in Engineering vol 2014Article ID 351496 6 pages 2014
[23] B Eravcg Automatic radar antenna scan analysis in electronicwarfare [MS thesis] Department of Electrical and ElectronicsEngineering Bilkent University Ankara Turkey 2010
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
International Journal of
AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
RoboticsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Active and Passive Electronic Components
Control Scienceand Engineering
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
International Journal of
RotatingMachinery
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporation httpwwwhindawicom
Journal ofEngineeringVolume 2014
Submit your manuscripts athttpwwwhindawicom
VLSI Design
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Shock and Vibration
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Civil EngineeringAdvances in
Acoustics and VibrationAdvances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Electrical and Computer Engineering
Journal of
Advances inOptoElectronics
Hindawi Publishing Corporation httpwwwhindawicom
Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
SensorsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Chemical EngineeringInternational Journal of Antennas and
Propagation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Navigation and Observation
International Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
DistributedSensor Networks
International Journal of
Top Related