Dynamic Online-Calibrated Radio

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direct Line-of-Sight (LOS) [15], [16], [19] between transmit-ter and receiver, which is not suitable for indoor scenarios.Angle-based techniques depend on estimating the angle ofarrival of RF signals from reference transmitters to estimatethe location of a receiver [17], [18]. Unfortunately, this is notsuitable for indoors as well because LOS condition does notexist in almost all indoor scenarios [15]. Power-basedtechniques use signal power (Received Signal Strength or

RSS) changes to estimate distance [17], [18], [11]. Twopower-based methods exist; path loss and fingerprinting[17], [18], [11]. Path loss method relates the distance from atransmitter to the received signal power measured by areceiver. In free space, log-distance path loss models [17],[18], [20], [21] are commonly used. However, because inindoor environments LOS condition almost does not existand different obstacles may exist on different orientationsaround RF transmitters, and due to the path loss modelinvariance to receiver orientation [49], it is difficult to modelsignal power changes indoors using only a simpleparameterized path loss mathematical formula.

1.2 Fingerprinting, Opportunities and ChallengesAccording to recent published results and literature surveys[8], [17], [18], [22], [23], [45], fingerprint methods outper-form other methods and techniques in indoor positioningscenarios. It can provide 1-2m accuracy indoors that fulfillsmany LBS indoor applications. Fingerprint wireless posi-tioning method [22], [23] is performed on two phases;offline phase and online phase. In offline phase, powerpatterns received at reference locations in the targeted areafrom visible WLAN APs are stored in a database calledradio map. This phase is known as site radio survey. Inonline phase, user power pattern (fingerprint) measured by

a WLAN-enabled device is used to conduct a search in theradio map to estimate a location using a variety oftechniques [17], [18]. The simplest common locationestimation algorithm used in this process is K-NearestNeighbors (KNN) [17], [18]. Although this scheme canachieve meter-accuracy indoors [22], [23], it requires adense radio map that accurately copies (rather thanmodels) the complex signals power characteristics indoors.Obviously, the offline site survey is impractical time-consuming process, especially inside large buildings anddynamic environments such as airports. Additionally, anddue to dynamic environments changes and the possibilityof moving some APs to new locations, the saved radio map

will be out-of-date eventually, and the whole offline sitesurvey needs to be repeated from time to time, which isimpractical and time consuming as well. These drawbacksprevent fingerprint wireless positioning systems from beingcommercially deployed and adopted.

1.3 An Adaptive Zero-Configuration System

To address these challenges in fingerprinting positioningtechniques, this paper introduces a novel system thatdynamically estimates a fine radio map based on onlinesignal power observations from the existing WLAN APusing Gaussian Process regression (GPR) [34], [35], whichis suitable for the RSS complex indoor stochastic char-

acteristics. To reflect most recent changes in the environ-ment without the need to perform offline site survey, wepropose a modification to the periodic beacon frame sentby APs to include (at low rates) AP location and RSS from

the other visible APs. Consequently, the system iscontinuously aware about recent changes in APs locationsand, without extra network hardware, the system does notrequire prior knowledge about the targeted indoorenvironment because online periodic observations roughlymodel the general shape and distribution of obstacles andobstructions in the environment.

GPR is performed on up-to-date observations to estimate

AP power profile. AP power profile is defined as AP signalpower (RSS) probability distributions over all locations inthe targeted area with predefined resolution. The radio mapis then constructed by merging all APs power profiles andapplying a fast feature reduction algorithm that selects themost informative APs to be used in positioning. Addition-ally, the estimation is continuously verified and corrected ina background process using few other online APs observa-tions. Moreover, Gaussian Regression is used to provide areliable statistical accuracy measure that is converted intoerror STDV in meters providing a reliable integritymonitoring [30] and enables other navigation systems to

be easily integrated with the proposed system usingfiltering algorithms such as Kalman Filter [24] or ParticleFilters [25].

2 RELATEDWORK

Recently, many approaches have been proposed to tacklethe problem of dynamic changes and complex RSScharacteristics in indoor environments. In [26], a feedbacksystem is proposed where an aiding dynamic motion modelis utilized as predictive model to predict the state of amoving object. In [26], a simplified pedestrian dynamicmotion model was used to predict user states in future.

Based on this prediction model, a Kalman Filter wasdeveloped and positions estimated by traditional finger-print positioning system were used as an update (measure-ment model) to the Kalman Filter [24]. The filtered outputsalong with recent RSS values are then taken as a feedback toupdate and improve the saved radio map. Similar work ispresented in [49], where a constrained linear Gaussianpedestrian dynamic motion model is used as a predictivestate probability distribution estimator. A Bayesian Filter isused to fuse this predictive probability distribution withprobabilistic nonlinear/non-Gaussian measurements up-dates from an RSS fingerprinting positioning system, andthe feedback from the filter is used to overcome the RSSvariations problem.

Although these methods decrease the effect of signalstrength variations indoors and keep an updated radiomap, they depend on a dynamic motion model, which is noteasy to obtain. Additionally, like any other feedback controlsystem, any small error in the dynamic motion model willpropagate to the radio map and magnified due to theexistence of the feedback loop, which deteriorates theaccuracy of the entire system significantly in a short time.

Some work has been done to entirely automate the sitesurvey process [10], [28], [29]. However, these methodshave some drawbacks. For example, in [28], a dynamic

radio map is estimated in a network-based positioningsystem provided that a full detailed CAD floor map of thebuilding is available with details about walls and roofs.Additionally, a ray tracing simulation software is used. The

ATIA ET AL.: DYNAMIC ONLINE-CALIBRATED RADIO MAPS FOR INDOOR POSITIONING IN WIRELESS LOCAL AREA NETWORKS 1775


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problem with this proposed scheme is that it depends onthe building details and it needs extra network hardware.This breaks the universality of the system and the ability todeploy it everywhere evenly and seamlessly. Furthermore,this scheme uses sniffers that listen to user devices signals,which limits the scalability of the system if the building isvery large and the number of users is huge.

Another interesting work to eliminate the need for sitesurvey is the work done in [10] in which an online path lossmodel was estimated and applied to estimate the distancefrom a mobile receiver to WLAN AP. However, asmentioned previously, the path loss model is too simpleto accurately cover and model the complex signal powerpatterns inside buildings. In [29], an autonomous mobilerobot that is equipped by ultrasound sensors and camera inaddition to WLAN card was used to perform SimultaneousLocalization and Mapping (SLAM) and, hence, record thesignal power patterns with the estimated location to finallyobtain a radio map of the environment in less time.Obviously this technique requires extra hardware and

errors associated with SLAM will propagate to the resultingradio map. Additionally, the mobile robot may not be ableto access all the WLAN area due to accessibility limitationsor security regulations.

3 RESEARCH OBJECTIVES

Motivated by the existing research results and challenges,the objectives of this work are as follows:

. Providing a complete WLAN indoor positioningsystem that dynamically constructs radio mapswithout need for offline site radio survey

. No prior knowledge or maps about the building orextra network hardware should be needed or used.

. The system should autonomously and continu-ously adapt to dynamic environment changes andsignal variations.

. The system should also be able to determine the mostinformative WLAN APs to perform best positioningin less computation time with fewer APs.

. The system must provide reliable integrity monitor-ing [30] in the form of an accurate error STDV ofposition estimations.

. It is also required that the system can be implemen-ted evenly and easily on any type of WLANs.

. The system should maintain a meter level accuracyof 1-2 m to fulfill LBS and other indoor positioningservices accuracy requirements.

4 METHODOLOGY

4.1 System Components

System hardware consists of the following main parts:

4.1.1 The WLAN

It consists of multiple APs that broadcast periodically (at alow rate to avoid overloading the network) a management

frame that contains RSS information for positioningpurposes. The format and method of collecting and sendingthese management frames will be described shortly in thesubsequent sections.

4.1.2 A Processing Unit

It can be a centralized computer server or even a mobiledevice. It is used to perform the following two processes:

. Construct dynamically a fine radio map from fewonline observations broadcasted periodically overmanagement frames from the existing WLAN APs.

. Process users requests to estimate locations anderror STDV based on current users power pattern(fingerprint).

4.1.3 A Communication Media

It may be the WLAN itself or the existing corporate TCP/IPLAN [5], [48], or even the internet. It can be any existingcommunication media that can be used to do the following:

. Enables the WLAN APs to send RSS observations tothe processing unit.

. Enables users WLAN-enabled devices to commu-nicate with the processing unit to obtain a position.

4.1.4 A Mobile User Unit

It is any WLAN-enabled device that can do the following:

. Scan the area for visible APs, decode the APsbeacon frames, and extract RSS information (userpower fingerprint).

. Send user power fingerprint to the processing unitover the communication media.

. Receive location information from the processingunit over the communication media.

4.2 System WiFi Prototype

Although the proposed system can be implemented on anytype of WLAN or wireless sensors network, we describe thesystem design and implementation on an IEEE 802.11WLAN (WiFi) and the corporate TCP/IP LAN, which isconnected to the WLAN as the communication media and acentralized computer server as the basic processing unit

(see Fig. 1). WiFi was selected for system physicalprototyping because it is free and available almost every-where and it provides TCP/IP LAN and internet con-nectivity [5], [6]. In our prototype implementation, the

1776 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 12, NO. 9, SEPTEMBER 2013

Fig. 1. System deployment.


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corporate TCP/IP LAN/IEEE 802.11 WLAN has beenutilized as communication media between devices becausethis type of network connectivity is available almosteverywhere from small homes and university campuses tolarge airports and even cities. Entire cities will be coveredby WiFi soon [47]. Also all new laptops, PCs, and handhelddevices such as cell phones and notebooks are WiFi-enabled

[5], [31], [32], [33]. So, the user unit could be any of thesedevices. Furthermore, the selection of WiFi makes thesystem more practical and cost-effective because WiFihardware is available at low cost.

4.3 IEEE 802.11 WLAN Beacon Frames

AP in WiFi networks is the main wireless connectivityprovider unit. According to the IEEE 802.11 Standards [6],[7], [33], AP periodically broadcasts a management framecalled beacon frame [6] that can be received and decoded byWiFi-enabled devices. IEEE 802.11 beacon frame is asubtype of management frames that contain importantinformation about the AP. This periodic message is similar

to the periodic message that is broadcasted by satellites toreceivers in a GNSS system [1], [2], [3]. A beacon frameconsists of fixed control segments and a variable lengthframe body. Frame body contains fixed section callednoninformation element for more control parameters forwireless connectivity purposes. The information elementsection contains variable-length vendor-specific generic-purpose data.

According to latest IEEE 802.11 standard document [7], atotal of 142 information elements are currently used and theremaining 113 information elements are reserved for futureuse. Among the important pieces of information sent on

the beacon frame is the Media Access Control (MAC)address of the AP which is a unique identifier set by the APmanufacturer. When WiFi-enabled device scan the area forvisible AP, it receives these beacon messages and the WiFiinterface card records the Received Signal Strength Indi-cator (RSSI).

4.4 Data Acquisition

Power pattern recording is defined as the process ofscanning the area for visible APs and extract their MACsand their RSSIs. In the traditional offline radio site survey[17], [18], [23], power pattern recording is performed

manually by an operator at reference locations normallyin a grid with sufficient resolution that covers the targetedarea. To overcome this impractical time-consuming offlinephase, the proposed system uses the fact that any AP isequipped by IEEE 802.11 WLAN transceiver hardware.Thus, in addition to its default functionality as a wirelessconnectivity provider, AP can also perform power patternrecording. Moreover, if the AP firmware is modified tocarry the power pattern recording results on the beaconframes over the free information element section [7], an APcan be seen as a reference location that periodicallybroadcasts the most recent power pattern recordings at

its location. Additionally, these online power patternrecordings can be sent periodically over any propercommunication media such as the corporate TCP/IPLAN to the centralized computer server.

In the proposed system prototype implementation, anddue to difficulty to modify AP firmware to broadcast thepower patterns recording results over the free informationelements of the beacon frame as described above (as this isrequired to be adopted by an AP manufacturer), we put awireless monitor beside each AP to perform power pattern

recording and send periodically every 1 second over thecorporate TCP/IP LAN to the centralized computer serverthe following information:

1. APs own MAC and APs own location.2. Neighboring APs MACs.3. Neighboring APs RSSIs.

Note that the wireless monitors are not a mandatory part ofthe system and they are used here just for systemprototyping and proof of concept. In the commercialimplementation, the power pattern recording will becarried over the beacon frames themselves to be receivedand decoded by any WiFi-enabled device.

The incoming periodic online power patterns recordingare filtered using a low-pass filter and arranged with theknown locations of the sending APs in a data table asshown in Table 1.

4.5 AP Power Profiling

Dynamic Radiomap construction is implemented by esti-mating a power profile for each AP so that at any location,RSS value can be estimated. Fig. 2 shows RSS values for anAP given by Table 1 in a real environment. Although itprovides small number of RSS observations, it gives ageneral idea about the current distribution of this AP power

profile. Estimating the power profile of each AP usingonline observations of Table 1 solves three problems atonce. 1) Handling the dynamic changes. 2) Modeling thegeneral shape and distribution of obstacles and obstructionsin the environment. 3) It keeps the system aware aboutrecent APs locations.

4.5.1 Log-Distance RSS Model Limitations

Although the general pattern of AP power pofile is alogarithmic decay [51], the complex indoor structure causesodd RSS patterns that cannot be simply modeled by log-distance formulas. This is obvious in Fig. 3, where curve

fitting fails to accurately estimate RSS values of Fig. 2 usingthe path loss models [41], [17], [18], [20], [21] given by

pd A B: logd=do; 1


TABLE 1Online RSS Observation Table


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wheredis the distance from AP, anddois initial distance of

measurements. Another limitation in log-distance models isthat it does not provide an accuracy measure with eachprediction and it does not consider orientation.

4.5.2 Bayesian Modeling Using Gaussian Process

Due to the limitations mentioned above, a better approachis proposed to build an optimized power profile for eachAP is to augment log-distance formulas with a nonlinearmodeling technique to model the residuals that cannot bemodeled by log distance or any other parametric formulas.In this problem, GPR [34], [35] is proposed as a nonpara-metric probabilistic modeling approach, which is very

suitable to the noisy nature of RSS values. GPR has beenrecently used to solve complex machine learning problems[36], [37], [38]. In Section 4.5.2, we describe the GPRconcepts and how the AP power profiling problem can bemodeled using GPR.

Gaussian Processes (GPs). A GP is a vector of randomvariables X, any finite number of which has a jointGaussian distribution [34] determined by a mean functionmxand covariance functionkx; x0, wherex2 X. A noisyprocess can be expressed as

Y fX "; 2

where fY ; Xg is a training data set. Assuming " is anadditive zero-mean Gaussian noise with covariance2n, anyorbitrary function,Ycan be modeled as a GP. An importantfeature of GPs is marginalization [34], [35] that enables us tocalculate the posterior probability at unknown inputs x ifsome observations are available from of the noisy functionat some given inputs x.

Standard Gaussian Linear Regression Model. The linearregression model for (2) can be written as

fX XTW ; 3

whereX is an input vector, W is a weight vector, f is theestimated process output. In Bayesian analysis, best weightsare the weights that maximize the likelihood function that isthe probability density of the observations given the

parameters (weights) that is given by the following formulaassumingn independent observations:

pYjX; W Yn

i1

pyijxi; W: 4

Equation (4) can be written as

pYjX; W Yni1

1ffiffiffiffiffiffiffiffiffiffiffiffiffi2Q

np exp yi xTiW

22n

12Q

2nn=2exp 122n jY X

TWj2

;

5

which is a Gaussian distribution of mean XTW and acovariance2nI. The prior probability density function (PDF)of weights is Gaussian with zero mean and covariance

Xp

:pW N0;X

p

: 6

The posterior PDF of weights is given by Bayes rule

pWjY ; X pYjX; WpW

pYjX : 7

Substituting from (5) and (6) in (7), noting that pYjX is anormalization factor, the posterior PDF of weights isGaussian and given by

pWjX; Y /N W 1

2nA1X Y ; A1 ; 8

whereA 2n XXT 1p .

To compute a predictive posterior PDF for new inputs x,we average the output over all weights with their posteriorprobabilities as follows:

pyjx; X ; Y

Z pyjx; WpWjX; YdW

Z xT WpWjX; ydW

N 1

2n

xT A1X Y ; xT A

1x

:

9

Nonlinear GPR. To overcome the limitation of linearmodels, inputs XX could be projected into higher dimen-sional feature space in which the problem becomes linearly


Fig. 2. Power values at other APs locations used for power profileestimation.

Fig. 3. Log-distance modeling of RSS online measurements of AP givenin Fig. 2.


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separable. Actually this is what the hidden layer does inmultilayer perception neural networks [50]. Thus, thefunction x is used to map the D-dimensional inputvectors to N-dimensional feature space. In this case, themodel is given by

fX XTW ; 10

and the predictive posterior PDF for unknown inputs x

given observationsXandy is obtained by

pyjx; X ; Y

N

xTpX

K 2nI

1Y ; x

Tpx

xTpXK

2nI

1XTpx

;

11

whereK XTpX.

Gaussian Kernel Learning. In (11), the termx

TPp Xcan be seen as a covariance function or kernel [34], [35]. In

Bayesian analysis, instead of learning the weights like thecase in neural networks [50], Gaussian Regression learnsthe kernel (Covariance of training data) [34]. This has theadvantage of having a nonparametric nonlinear regressionmodel that is robust to observations noises. The mostcommon kernel covariance function is the exponentialgiven by

Covyi; yj kxi; xj 2ni j 12

kxi; xj 2

fexp

1

2 xi xj

T

Mxi xj

; 13

where xi; xj2 X and yi; yj 2 Y and is the Delta Diracfunction. Parameters 2n,

2f, and M are called the hyper-

parameters and they can be learned and optimized bymaximizing the likelihood [34], [35] function given by

LogpYjX 1

2YT

K 2nI

1Y

logK 2nI n2log 2;

14

whereKKis the covariance matrix over all input vectors inXX. However, we found that using only likelihood to learn

these parameters does not necessary get best positioningaccuracy. Thus, a genetic algorithm will be used to optimizethese hyperparameters as described in section OnlineVerification and Calibration.

4.5.3 AP Power Profiling Using GPR

RSS Prediction with Zero-mean GPR. First, we will considerRSS observations for each AP in Table 1 has a zero-meanGaussian prior PDF. The training data for each AP consistof pairs {x1; y1; x2; y2 . . . xN; yN}, where x i s a 2 Dlocation, and y is an RSS value of the AP at location x.Initially, a covariance NbyNmatrixK is calculated using(12) over the data set of N observations (pairs of 2DLocations and RSS values) available in Table 1. Having thecovariance matrixKfor all gathered data set (X,Y), signalpower PDF of this AP in unknown inputs x can beestimated according to Bayesian rule (marginalizationfeature [34]) as follows:

x kx; XK

2nI

1Y ; 15

2x k

x; x

k

x; XT

K 2nI1

kx; X; 16

where x is the predicted mean RSS (in dBm) at thislocation, x is the STDV (in dBm), kx

; X is a vector ofNelements each element is the result of applying (13) onx and the corresponding element in X. The estimatedpower profile and corresponding error STDV for the AP ofFig. 2 is shown in Figs. 4 and 5, respectively. It is importantto note that GPR performs three important functions asfollows: 1) Predicts power PDF over all locations.2) Smoothes out the power values noise. 3) Provides aSTDV which each predicted power value. It is important tonote also that Fig. 5 shows that the STDV decreases in andaround the locations where there are APs (there are powerpatterns observations) and decreases gradually as thelocation goes away from the locations in which we haveAPs (power patterns observations). This characteristic isvery important to provide a reliable accuracy measure withlocation estimations.

RSS Prediction with Log-Distance mean GPR. It is obviousfrom Fig. 4 that the RSS values at locations in which we donot have training data (far away from any AP), theestimated RSS value tends to zero. This is because thezero-mean assumption in GPR (mx 0). Thus, to enhance

RSS estimation, we need a proper mean. A good idea is touse the Log-Distance model of (1) as general mean and thenuse GPR to model the residual RSS errors that cannotbe modeled by the Log-Distance model. In this case, the


Fig. 4. Power profile estimation using GPR with zero-mean.Fig. 5. STDV of power profile estimation.


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training data to GPR are not the observed RSS value but the

difference between observed RSS value and the estimatedRSS value by the Log-Distance model. The new RSSEstimated power profile for the AP of Fig. 2 using GPRwith Log-Distance mean is shown in Fig. 6. Clearly, thetendency to zero is removed.

In the GPR with Log-Distance mean case, the predictedresidual RSS for unknown location x will be given by

x mx kx; X

K 2nI

1Y mX; 17

mx A B: logx rAP=do; 18

where x rAP

k k is the distance from the AP location rAPand the input location x. Note that the variance formula

will be the same as in (16). Also note that modelparameterrs of (18) will be estimated using curve fittingbased on the data points available in Table 1. It can be seenclearly that the tendency to zero in points away from APs isno longer there. In the experimental results and discussionssection, we will compare the performance in both cases(zero-mean GPR and Log-Distance mean GPR).

4.6 Online Radio Map Construction

The location computer server will construct the radio mapby merging all estimated APs power profiles so that foreach location x, there is a corresponding vector of powerprobability distribution from all AP in the targeted areavisible from this location. The constructed radio map coversthe whole targeted area will be saved in a larger databasetable. Additionally, with each location in the fine radio map,the average of STDVs of the individual AP power PDFs isstored as the STDV associated with this location.

4.6.1 Online Verification and Calibration

The verification process will be implemented as follows:

1. In radiomap construction process, instead of using

the whole data in Table 1, a portion such as80 percent will be used and the remaining 20 percentwill be used to verify the accuracy of the constructedradio map.

2. The testing RSS values are used to estimate alocation using constructed radiomap using a simpleK-NN Algorithm.

3. These locations are compared with the referencelocations of these testing data and the location meansquare error (Loc RMSE) will be recorded.

4. If Loc RMSE is still larger than a threshold, the

hyperparameters 2n, 2f, and M used in each APprofile estimation will be changed according toGenetic Algorithms operators [43], [46] using thefollowing fitness function to be maximized:

Fn; f; M w1

Loc RMSE w2LogpYjX: 19

Note that in the fitness function in (19), we weight thelikelihood over the training data and the positioningaccuracy over the testing data using weights w1 and w2tuned empirically. In genetic iteration, AP power profilingand radiomap construction will be repeated and new

radiomap will be used again with the testing data set toverify its accuracy. This process continues until anacceptable mean square error is obtained. This process isillustrated in Fig. 7.


Fig. 6. Power profile estimation using Gaussian Regression with Log-Distance Mean.

Fig. 7. Online calibration of dynamic radio map.


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4.6.2 AP Selection Using FOS Feature Reduction

In fingerprinting, a high number of APs may deteriorateaccuracy by incorporating insignificant APs power valuesthat does not contribute in discriminating between loca-

tions [39]. Additionally, it is preferred to remove anyredundancy from the radiomap to avoid unnecessaryprocessing. In [39], a feature reduction algorithm basedon transformation matrix was proposed. However, onedrawback of this technique is that the original feature space(the AP MAC) is lost. Additionally, it is computationallyexpensive. In Atia et al. [45], the authors introduced a fastfeature reduction approach using Fast Orthogonal Search(FOS) to simultaneously fit multiple observations [44] forthis purpose. It is a faster alternative to PrincipalComponent Analysis [22] that selects principal features inthe original feature space itself without performing Eigenvalues/vectors calculation or transformation. In this step,

this fast features reduction approach is applied to select themost informative AP in the constructed fine radio map.

4.7 Positioning Procedure

The positioning scenario will be performed using thefollowing steps:

4.7.1 Positioning Request

The WiFi-enabled device will perform a wireless scanto collect a power fingerprint. A power fingerprint isdefined as MAC/Power value pairs from all surroundingvisible AP. The WiFi-enabled device sends this powerfingerprint to the location server using an XML web request

(web-service request [42]).

4.7.2 Position Estimation

The location server will compare the current sent powerfingerprint with the saved power patterns in the radio mapusing a weighted-KNN algorithm [17], [18] to provide alocation. In a weighted-KNN algorithm, a weighted averageis performed by giving the highest weight to the mostnearest radio map point. Thus, given current WiFifingerprintRSSIc, the current position Pc is estimated by

Pc w1P1 w2P2 wkPk 20

wi expRSSIc RSSIi

2Pk1expRSSIc RSSIj

2; 21

where RSSIi is the WiFi power pattern recorded withpointPi in the radiomap.

4.7.3 Error STDV Calculation

The location error STDV is calculated by converting thepower STDV estimated by GPR into meters. As shown in

Fig. 3, a Log-Distance pathloss model is fitted on each APRSS values in Table 1. To calculate distance change dcorresponding to power changep, from (1) we have

pd

@

A B: log

ddo

@d

B

dln10:

22

Then, we have

d pdln10

B : 23

The location error STDV is calculated by averaging theSTDVs of the K nearest neighbors selected in the radiomappoints used in location estimation in (20).

4.7.4 Positioning Response

The location server sends the computed location and errorSTDV to the WiFi-enabled user device using an XML webrequest (web-service request [42]).

5 EXPERIMENTAL RESULTS AND DISCUSSIONS

5.1 Experimental Setup and FOS-Selection of APs

Physical experiments were performed in two indoor areas;the first one is inside the Queens university residence. It isapproximately 20 m by 15 m area (see Fig. 8). The secondenvironment is 25 m by 40 m in sixth floor in QueensUniversity in Computer Engineering Department, Canada(see Fig. 9). Both environments are equipped by IEEE802.11 WLAN. Wireless monitors were distributed besideeach AP. The environments are connected by the uni-versity TCP/IP network. Part of the network is IEEE 802.11WLAN and part is wired, which is the university EthernetTCP/IP LAN.

A computer server running a web service was setup to

process XML web requests from wireless monitors andusers WLAN-enabled devices. The wireless monitors sendpower pattern recordings every 1 second to the computerserver through XML web requests. The computer server


Fig. 8. Testing area 1.

Fig. 9. Testing area 2.


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applies low-pass filtering to RSS measurements. Thiscomputer server runs the dynamic online-calibrated radiomap construction process in the background and run alsothe FOS-selection of APs. The FOS-selected APs are shownin red circles in Figs. 8 and 9. All RSS estimation andpositioning will use those four APs.

In parallel, another web service is running to receiveusers positioning requests through XML web requests.The location estimation web service uses the most recentonline-calibrated radiomap to process users requests andresponds by location estimation and STDV (estimationaccuracy measure).

5.2 Obstacles Distribution Effect on RSS

To see how GPR broadly models the general shape of thearea, a map of AP3 in area 2 overlaid with area 2 map isshown in Fig. 10. An odd power decrease-then-increase isseen in west south corner of the area. The explanation forthis is that there is an electricity room beside this area as

shown in Fig. 10. Additionally, estimated RSS values in thewest north room generally show the effect of walls. Theseodd RSS patterns cannot be modeled by traditional passloss models.

5.3 RSS Estimation Accuracy

To assess the RSS estimation accuracy, a total of 58 and 67reference locations were selected in the two testing areas,respectively, (see small dark circles in Figs. 8 and 9) atwhich a WLAN-enabled laptop was used to record RSSIvalues from visible APs. Then, the observed RSS at theselocations are compared to the GPR estimated RSS. The

overall results are summarized in Tables 2 and 3 showingGPR without and with Log-Distance model mean. Theresult s of all eight AP from area 1 and area 2 are shown inFig. 11 in GPR with Zero-mean GPR and Log-Distance

mean case including the STDVs. It can be seen from

numbers in Tables 2 and 3 the RSS error is reduced by 40-50 percent if Log-Distance GPR is used. It is noted alsothat accuracy decreases at right borders area of testingArea 2 because features reduction did not select any APson right borders.

5.4 Relationship between RSS Error and STDV

The bottom graphs in Fig. 11 show that there is a suitabledegree of correlation between RSS errors and the estimatedaccuracy measure (RSS STDV). In regions with large RSSerrors, SDTV is higher and vice versa. This reflects therelevancy of STDV as an accuracy measure.

5.5 Location Estimation Accuracy5.5.1 Static Test

In this test, the observed RSS values at the 58 and 67reference locations were sent to the location computer serverthat used the recently constructed radio map to provide alocation and STDV for each reference location. Position error(RMSE) in testing area 1 and area 2 are shown in Table 4,which shows how Log-Distance GPR performs much betterin positioning than zero-mean GPR. Fig. 12 shows the resultsif the radio map is constructed using Log-Distance meanGPR. RMSE of 2.0367 m in area 1 and 3.017 m in area 2 wereobtained. These results are very similar or even slightly

better than the results reported in [28] with the advantage ofnot depending on any preknowledge of the building(i.e., floor maps) and without ray-trace simulation software.Also the results are comparable to those reported in [52] inwhich similar Gaussian Regression approach is used butwith offline radio survey phase. Our approach has theadvantage of not depending on offline training data thatenable it to approach to dynamically model the changes inthe environment using less AP.

Relationship between Positioning Error and Estimated Stan-dard Deviation. Fig. 12 shows the position STDV values witheach testing point. In general, there is a suitable degree of

positive correlation between the estimated STDV and thetrue accuracy. This confirms that, for positioning as well, ingeneral, the error STDV is consistent with the error value,which, again, indicates the reliability of the STDV calculated


Fig. 10. AP RSS estimated distribution from the FOS-APs.

TABLE 2RSS Errors (dBm) in Testing Area 1

TABLE 3RSS Errors (dBm) in Testing Area 2


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5.5.2 Walking Test

Due to the lack of a reliable accurate reference navigation

system indoors, we followed predefined trajectories with

known Waypoints. These predefined trajectories are shown

in Figs. 8 and 9 for testing area 1 and area 2, respectively.

During this test, a WLAN-enabled laptop was used to

record users power fingerprint and send this information

over XML web requests to the location computer server.

The laptop is connected wirelessly to the university IEEE

802.11 WLAN and can access the location computer server.The laptop has software that collects current user power

fingerprint and issues an XML web request and sends it to

the location computer server, which responds by location

and error STDV. At the known waypoints, we stopped andrecorded the reference location for positioning RMSEcalculation purposes.

Fig. 14 and Table 5 show the positioning accuracy of thewalking test in area 1 and area 2. The RMSE in area 1 is2.26 m and 3.196 m in area 2. These results compared tosystems in [28] and [52] considered very good withoutoffline surveys or ray tracing or floor maps software. Fig. 14shows again the positive correlation between the positioningerror and the estimated accuracy measure (STDV), which isconsidered an added value and significant contribution.

To compare the performance with the traditional offlineradiomap, the positioning of the two trajectories in testingarea 1 and area 2 were performed using the 58 and 67offline radiomap points. The RMSE are shown in Table 5,which shows comparable performance with the advantageof removing the time-consuming offline surveying work.

5.5.3 Maximum Expected Positioning Error

If the area contains single AP, the radio map will be the AP

power profile itself, which will be a bell centered at the APlocation (Fig. 15). Given any positioning request containsthis AP RSS value, matched locations in the radio map willbe a perfect circle centered at AP location and the system


TABLE 4Positioning ErrorStatic TestArea 1 and Area 2

Fig. 12. Static test positioning error at reference locations at testing area1 and area 2.


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will always generate the AP location (average of circile

border is the circle center) as a solution. The maximumposition error would be the radius of the coverage circle of

the AP. So, the stronger the AP signals is the larger will be

the positioning uncertainty.

6 CONCLUSION

In this paper, a WLAN-based positioning system suitablefor indoor and GNSS-denied area was introduced. The basicadvantage of the proposed system is that it uses few onlinepower patterns measured by the WLAN AP themselves toconstruct dynamically a fine radio map that accurately

models the signal power distribution over environmentwithout the need for the traditional time-consuming offlinesite survey or extra network hardware. Although many APsmeasurements were used to dynamically estimate the fine

radio map, only four AP were selected by featuresreduction step to achieve best positioning accuracy in lesstime. Of course in larger areas such as airports, more APwill be needed accordingly. Thus, in addition to the

continuous dynamic adaptation to environments changesby the verification and correction process, the system usesfew wireless AP to do positioning. Another important


Fig. 14. Walking test results in area 1 and area 2 using Log-DistanceGPR RSS radio map estimation.

TABLE 5Positioinig ErrorWalking Test Area 1 and Area 2

Fig. 13. Relationship between the estimated accuracy measure (STDV)and actual error in both testing areas.

Fig. 15. Power profile if only single AP exists.


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advantage of the proposed system is the ability to provideaccurate error STDVs that tells the user how confident thesystem is about the estimated positioning results. Thismakes the system easy to integrate with other filtering-based integrated navigation systems that depend on errorSTDVs to weight the observation and determine itscontribution in the final integrated output. Two physicalexperiments in two different areas were conducted, and the

results were consistent. In both testing areas, 2-3m accuracywas achieved with few APs.

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