Application of modern guidance control theory to a back-to ... · Calhoun: The NPS Institutional...
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Calhoun: The NPS Institutional Archive
Theses and Dissertations Thesis Collection
1984
Application of modern guidance control theory to a
back-to-turn missile.
Shin, Bohyun
Monterey, California. Naval Postgraduate School
http://hdl.handle.net/10945/19161
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T218334
M i\ \l AuaA-JIJ
Monterey, Califo
t SGHOO -
APPLICATION OF MODERN GUIDANCE CONTROL THEORYTO A
BANK-TO-TURN MISSILE
bv
Bohyun Shin
March 19 84
Thesis Advisor: Daniel J. Collins
Approved for public release; distribution unlimited
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UNCLASSIFIEDSECURITY CLASSIFICATION OF THIS PAGE (When Date Entered)
REPORT DOCUMENTATION PAGc READ INSTRUCTIONSBEFORE COMPLETING FORM
I. REPORT NUMBER 2. GOVT ACCESSION NO 3. RECIPIENT'S CAT ALD3 -J.
4. TITLE (and Submit)
Application of Modern Guidance ControlTheory to a Back-To-Turn Missile
5. TVE OF REPORT 4 PERIOD COVEREDEngineer's Thesis;March 19 3 4
6. PERFORMING ORG. REPORT NUMBER
7. AUTHOR^;
Bohvun Shin
8. CONTRACT OR GRANT NUMBERS;
i- PERFORMING ORGANIZATION NAME AND ADDRESS
Naval Postgraduate SchoolMonterey, California 93945
10. PROGRAM ELEMENT. PROJECT, TASKAREA 4 WORK UNIT NUMBERS
11. CONTROLLING OFFICE NAME AND ADDRESS
Naval Postgraduate SchoolMonterey, California 95943
12. RE°ORT DATE
March 19 8 413. NUMBER OF PAGES
170If. MONITORING AGENCY NAME 4 AOORESSff/ dllterent from Controlling Ollice) IS. SECURITY CLASS, (ot this report)
Unclassif i edIS«. DECLASSIFICATION DOWNGRADING
SCHEDULE
16. DISTRIBUTION STATEMENT (ot thli Report)
Approved for public release; distribution unlimited
17. DISTRIBUTION STATEMENT (ot the abstract enttrtd in 3loek 20, It different trocn Report)
18. SUPPLEMENTARY NOTES
19. KEY WOROS (Continue on reverse aide // r.ectasary and Identity by block number)
Optimal control, missile guidance, bank-to-turn missile
20. ABSTRACT (Continue on reverse aide it necessary end identity by block number)
In this work, the control laws of a bank-to-turn missile usingan optimal estimator in the terminal guidance phase weredesigned, and the effect of increasing the number of measurementsensors in the missile to generate more information on the statewas investigated. In the design of the control law the modernoptimal control theory with the a quadratic performance index wasused. Implementation of this control law required the use of a
do ,;FORMAN 73 1473 EDITION OF 1 NOV 65 IS OBSOLETE
S'N 0)02- LF- 014- 6601UNCLASSIFIED
SECURITY CLASSIFICATION OF THIS PAGE (When Dele Entere<
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UNCLASSIFIEDSECURITY CLASSIFICATION OF THIS PAGE (Wftmn Datm Ent»r»d)
Kalman filter as the optimal estimator. The extended Kalmanfilter algorithm was utilized in the present stud}* since themeasurement states were non-linear functions of the statevectors (relative distance, velocity and target acceleration).In order to test the effects of the implementation of theincreased measurement sensors, two-, fcur- and six-measurementsensors were assumed to be implemented in the optimal estimatorBy computer analysis, the designed guidance laws were evaluatedand the effect of the implementation of increased measurementsensors was tested. Miss distance was chosen as a performancestandard during the simulation.
The results of the simulation revealed that the designedguidance law was successful within the specified scenarios,the effect of the implementation of increased measurementsensors for the estimator was favorable only in that increasedmeasurement sensors generated more information about the statevectors, but as the Kalman filter algorithm became more complex,the estimator performance was not enhanced to the degreeexpected with increased measurement sensors.
N 0102- IF- 014- 6601
2 TINn.A.SSTPTFnSECURITY CLASSIFICATION OF THIS PAGEP»7i»n Dal» Znfrad)
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Approved for public release; distribution unlimited
Application of Modern Guidance Control Theoryto a
Bank-To-Turn Missile
Bohyun ShinMajor, Republic of 'Korea Air Force
S., Republic of Korea Air Force Academy, 1975
Submitted in partial fulfillment of therequirements for the degree of
AERONAUTICAL ENGINEER
from the
NAVAL POSTGRADUATE SCHOOLMarch 19 84
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ABSTRACT
In this work, the control laws of a bank- to- turn mis-
sile using and optimal estimator in the terminal guidance
phase were designed, and the effect of increasing the number
of measurement sensors in the missile to generate more
information on the state was investigated. In the design of
the control law the modern optimal control theory with the a
quadratic performance index was used. Implementation of
this control law required the use of a Kalman filter as the
optimal estimator. The extended Kalman filter algorithm was
utilized in the present study since the measurement states
were non-linear functions of the state vectors. In order to
test the effects of the implementation of the increased mea-
surement sensors, two-, four-, and six-measurement sensors
were assumed to be implemented in the optimal estimator. By
computer analysis, the designed guidance laws were evaluated
and the effect of the implementation of increased measurement
sensors was tested.
The results of the simulation revealed that the designed
guidance law was successful within the specified scenarios,
the effect of the implementation of increased measurement
sensors for the estimator was favorable only in that
increased measurement sensors generated more information
about the state vectors.
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TABLE OF CONTENTS
I. INTRODUCTION 13
II. KINEMATICS OF A BANK-TO-TURN MISSILE 15
A. ASSUMPTIONS 15
B. MISSILE CONTROL METHOD 16
C. GEOMETRY OF MOTION IN SPACE 17
D. KINEMATICS OF CONTROL \~ECTOR 21
E. CO-ORDINATE TRANSFORMATION 2 7
III. AN OPTIMAL CONTROLLER 30
A. GEOMETRY 30
B. SUMMARIZED DESCRIPTION OF AN OPTIMALCONTROLLER 32
C. APPROXIMATING TIME -TO -GO 5 5
D. PROJECTED-ZERO-CONTROL MISS (PZC MISS)DISTANCE 36
E. SIMULATION RESULTS 59
IV. OPTIMAL ESTIMATOR 54
A. DESCRIPTION 54
B. STATE EQUATIONS 55
C. MEASUREMENT EQUATION 62
D. THE EXTENDED KALMAN FILTER 69
E. INITIALIZATION OF THE KALMAN FILTER 76
1. Initializing The Error CovarianceMatrix, P 77
2. Initializing The Measurement NoiseVariance Matrix, R 79
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F. EVALUATION OF THE OPTIMAL ESTIMATORPERFORMANCE 80
V. PERFORMANCE EVALUATION OF THE CONTROL SYSTEM ... 120
VI. CONCLUSIONS 150
APPENDIX A: PROGRAM LISTING 152
LIST OF REFERENCES 169
INITIAL DISTRIBUTION LIST 170
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LIST OF FIGURES
2.1 MISSILE CONTROL SYSTEMS IN POLAR CO-ORD 17
2.2 GEOMETRY OF RELATIVE MOTION 18
2.5 RELATIVE VELOCITY COMPONENTS IN POLARCO-ORD 22
2.4 KINEMATICS OF CONTROL VECTORS VIEWED FROMREAR OF MISSILE 23
2.5 KINEMATICS OF CONTROL VECTORS VIEWED FROMSIDE OF MISSILE 2 3
3.1 COMPONENTS OF PZC, MISS DISTANCE 31
5.2 SCENARIO- 1 TAIL-CHASE ENGAGEMENT 42
3.5 SCENARIO-2 HEAD-ON ENGAGEMENT 45
5.4 SCENARIO- 5 SIDE-APPROACH ENGAGEMENT 44
3.5 NORMAL ACCELERATION COMMAND VS TIME 4 5
3.6 ROLL RATE COMMAND VS TIME 46
3.7 MISSILE BANK ANGLE VS TIME 47
5.8 NORMAL ACCELERATION COMMAND VS TIME 5!
5.9 ROLL RATE COMMAND VS TIME 52
3.10 MISSILE BANK ANGLE VS TINE 53
4.1 DISCRETE EXTENDED KALMAN FILTER TIMINGDIAGRAM 71
4.2 SYSTEM MODEL AND DISCRETE KALMAN FILTER 73
4.3 DISCRETE KALMAN FILTER INFORMATION FLOWDIAGRAM 75
4.4 ERROR COVARIANCE COMPONENT (Pll) VS TIME .... 84
4.5 ERROR COVARIANCE COMPONENT (P22) VS TIME .... 85
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4.6 ERROR COVARIANCE COMPONENT (P55) VS TIME 86
4.7 ERROR COVARIANCE COMPONENT (P44) VS TIME 87
4.S ERROR COVARIANCE COMPONENT (P55) VS TIME 8S
4.9 ERROR COVARIANCE COMPONENT (P66) VS TIME 89
4.10 ERROR COVARIANCE COMPONENT (P7 7) VS TIME 90
4.11 ERROR COVARIANCE COMPONENT (P88) VS TIME 91
4.12 ERROR COVARIANCE COMPONENT (P99) VS TIME 92
4.15 KALMAN GAIN COMPONENT (Gil) VS TIME 93
4.14 KALMAN GAIN COMPONENT (G21) VS TIME 94
4.15 KALMAN GAIN COMPONENT (G51) VS TIME 95
4.16 KALMAN GAIN COMPONENT (G41) VS TIME 96
4.17 KALMAN GAIN COMPONENT (G51) VS TIME 9 7
4.18 KALMAN GAIN COMPONENT (G61) VS TIME 9 8
4.19 KALMAN GAIN COMPONENT (G71) VS TIME 99
4.20 KALMAN GAIN COMPONENT (G81) VS TIME 100
4.21 KALMAN GAIN COMPONENT (G91) VS TIME 101
4.2 2 STATE ESTIMATE: Rx VS TIME 10 5
4.25 STATE ESTIMATE: Ry VS TIME 104
4.24 STATE ESTIMATE: Rz VS TIME 10 5
4.2 5 STATE ESTIMATE: Vrx VS TIME 10 6
4.26 STATE ESTIMATE: Vry VS TIME 10 7
4.27 STATE ESTIMATE: Vrz VS TINE 108
4.2 8 STATE ESTIMATE: AT x VS TIME 10 9
4.29 STATE ESTIMATE: AT y VS TIME 110
4.50 STATE ESTIMATE: AT z VS TIME Ill
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4.51 ESTIMATED TIME -TO- GO VS TIME 112
4.52 NORMAL ACCELERATION COMMAND A'S TIME 115
4.55 MISSILE NORMAL LOADING VS TIME 114
4.54 ROLL RATE COMMAND VS TIME 115
4.5 5 MISSILE ROLL RATE VS TIME 116
4.56 MISSILE BANK ANGLE VS TIME 117
5.1 MISSILE MAXIMUM ACCELERATION VS MEAN MISSDISTANCE 121
5.2 MISSILE MAXIMUM ACCELERATION VS MEAN MISSDISTANCE 122
5.5 MISSILE MAXIMUM ROLL RATE VS MEAN MISSDISTANCE 12 5
5.4 MISSILE MAXIMUM ROLL RATE VS MEAN MISSDISTANCE 12 6
5.5 MISSILE TIME CONSTANT VS MEAN MISS DISTANCE . . 128
5.6 MISSILE TIME CONSTANT VS MEAN MISS DISTANCE . . 129
5.7 ONE SIGMA ANGLE ERROR VS MEAN MISS DISTANCE . . 150
5.8 ONE SIGMA ANGLE ERROR VS MEAN MISS DISTANCE . . 151
5.9 MISSILE VELOCITY VS MEAN MISS DISTANCE 15 5
5.10 MISSILE VELOCITY VS MEAN MISS DISTANCE 154
5.11 ENGAGEMENT TIME VS MEAN MISS DISTANCE 156
5.12 ENGAGEMENT TIME VS MEAN MISS DISTANCE 15 7
5.15 MISSILE MAXIMUM ACCELERATION VS MEAN MISSDISTANCE 140
5.14 MISSILE MAXIMUM ROLL RATE VS MEAN MISSDISTANCE 141
5.15 MISSILE TIME CONSTANT VS MEAN MISSDISTANCE 142
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5.16 ONE SIGMA. ANGLE ERROR VS MEAN MISSDISTANCE 145
5.17 MISSILE VELOCITY VS MEAN MISS DISTANCE 144
5.18 ENGAGEMENT TIME VS MEAN MISS DISTANCE 145
5.19 ONE SIGMA .ANGLE RATE ERROR VS MEAN MISS DISTANCE 146
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LIST OF TABLES
4.1 NOMINAL PARAMETERS 31
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ACKNOWLEDGEMENT
The author wishes to express his sincere appreciation
to Professor Daniel J. Collins, whose assistance and
encouragement contributed immeasurably to this research.
The author also wishes to dedicate this thesis to his
wife, Eunsook. Without her constant support and under-
standing this work would not have been possible.
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I. INTRODUCTION
The bank-to-turn missile has high lift acceleration in
a direction perpendicular to its wings. For airbreathing
missiles which are required for large stand-off ranges it
also offers the advantage of lower inlet angles of attack
than that of skid-to-turn missiles.
Although the control laws using the modern guidance
control theory are more complex than the normally used
proportional navigation air-to-ground method, they have
great potential for maneuvering targets in air-to-air
engagement situations. The application of optimal control
and estimation theory to bank-to-turn missile configurations
has been tried in order to obtain increased performance. In
the application of the optimal control theory to the bank-
to-turn missile, it is necessary to have information on all
states or an estimate of the state variables that have not
been measured. Since the state information available from
the typical missile sensors is limited, it is necessary to
employ an estimator. The estimator used in this study was
a first order extended Kalman filter.
The present work addressed the design and evaluation of
the optimal state estimator and optimal control laws for
application to a bank-to-turn missile. In the development
of this work, Chapter 2 will describe the kinematics of the
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missile and formulate the equations of the motion. All
major assumptions are listed. The optimal controller is
described in Chapter 3. The simulation results of the
controller on three scenarios described later are used to
check the suitability of the optimal controller. The
optimal estimator is covered in Chapter 4, the formulation
of the state equation, measurement equations and the
derivations of the elements of Jacobean matrix are covered.
The extended Kalman filter algorithm for non-linear system
is then reviewed. Before the final computer simulation a
discussion is given on the nominal parameters needed in
initializing the Kalman filter. The results of the simula-
tion of estimators with two-; four-, and six-measurement
vectors on a typical scenario are then presented. In
Chapter 5, the simulation results of the control laws imple-
mented the estimator with two-, four- and six -measurement
sensors on three scenarios is described. The detail analy-
sis of the results is developed in order to investigate the
effect of the key variables of the control system on a bank-
to-turn missile. The mean miss distance determined from 50
Monti Carlo runs as a performance standard was used in the
analysis of the results of the control laws as a function of
the key variables. Finally, the conclusion are summarized
in the last Chapter.
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II . KINEMATICS OF A BANK -TO -TURN MISSI LE
A. ASSUMPTIONS
The geometry and the equations of motion of the missile
will be developed, representing the positions in space,
under the following assumptions:
1. The local geographic coordinate system will be used
as the inertial reference.
2. To simplify the equation of motion the stability
axes are adopted for the missile body.
3. The missile velocity due to thrust is constant.
There is no missile acceleration in velocity due to
thrust
.
4. Each control surface or surfaces is rigid.
5. Relative to the body axes, each control system has
only one degree of freedom. The missile's control
acceleration vector acts normal to the velocity
vector, that is, the dot product of acceleration
vector and velocity vector is zero, also the
acceleration acts through the missile center of
gravity
.
6. The Euler angles t> , 9 and <b will be used to° r v v
describe the orientation of the missile with
respect to inertial space, where <$> and 6r
are
horizontal and vertical flight path angles, and $
is the roll or bank angle.
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7. The first order lags with time constants r andP
x to a roll rate command P and a normalo c
acceleration command a will be considered forc
the dynamic response of the missile.
B. MISSILE CONTROL METHOD
Before going into the mathematical detail concerning
the motion of a missile in space as a result of a guidance
command, it is helpful to review with the control law for a
bank-to-turn missile. The guidance system detects whether
the missile is flying too much or too high to the left,
right or vertical. The guidance system measures these
deviations or errors and sends signals to the control system
to reduce these errors to aero. The task of the control
system therefore is to maneuver the missile quickly and
efficiently as a result of these signals. In a bank-to-turn
missile system, the guidance angular error detector produces
two signals R and <j> in terms of polar coordinate expres-
sion, showed in Figure 2.1. The same signals can be
expressed in another way, that is, in cartesian coordinate.
The usual method is to regard the <p signal as a command
to roll through an angle a measured from the vertical
and then to maneuver outwards by means of the missile's
elevators. The method of maneuvering the missile is as
follows. The $ command goes as a positive command to one
control surface and a negative command to the other, this
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MISSILE
TARGET
FIGURE 2.1. MISSILE CONTROL SYSTEM IN POLAR CO-ORD.
causes the missile to roll. The R command goes to
surfaces always as a positive demand, this causes the
missile to accelerate normal to the velocity vector. The
intention is to make the response in roll fast so that the
commands can be applied simultaneously which makes for
simplicity, only a pair of control surfaces are used as
ailerons and elevators at the same time, control is obtained
by means of the separated servos.
C. GEO.METRY OF MOTION IN SPACE
The motion of a missile as a particle may be described
by using coordinate measured with moving axes (relative-
motion analysis). The analysis of motion can be simplified
by using measurements made with respect to a moving coordi-
nate system. In present work, the equations of motion will
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K
MISSILE NORMAL
FORCE AXIS
7 MISSILELONGITUDINALAXIS
J MISSILE WING AXIS
FIGURE 2.2 GEOMETRY OF RELATIVE MOTION
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be described with respect to the initial missile body axes
taken as the inertial axes. After launching, the missile
body axes changes position, but the relative position,
velocity and acceleration are still computed in inertial
axes, that is, in the initial body axes. The current zero
effort miss distance is calculated. This will then be
transformed to values in missile body axes to compute the
amount of the control inputs to the missile using Euler's
transformation.
Figure 2.2 shows the missile and target as points with
respective vector velocity V and V\ . In this analysis,^ m t'
because IV I is assumed to be at least 2 1 V . I , and the1 m
'
' t ! '
angle of attack is assumed to be small, the lead angle <j>
between Vn and the missile longitudinal axis is small.
Let the vector denote the relative position of the target
with respect to the missile. The orientation of the sight
line vector in inertial space can be represented by the
angle y Rand 6
R , as shown in Figure 2.2. The sight
line vector is resolved into three components in inertial
axes as follow s :
Rx = R*cos(6R ) "ccs (y R) = Xt - Xm
Ry = R*cos(9R ) *sin(;jj
R) = Yt - Ym
Rz = - R*sin (cJ;R) t - Zm
(2.1)
(2.2)
(2.3)
Where R is the magnitude of the sight line vector.
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Define the relative velocity vector
V = \\ - V- r - 1 - in
•4)
Where V. and V are target and missile velocities.
The rate of change of the sight line vector's magnitude
is equal to the component of VR
along R . It can be
expressed as following:
R =R (2.5)
V *R + V *R + V *Rrx x ry >• rz :
(R2
+ R2
+ R2
)v x v z
(2.6)
Where V = \* - Vrx tx mx
V = V+ - Vrv ty mv
V , = V+ - Vrz tz m:
are the relative velocity components in the inertial frame.
In the geometry, the relative angles <Jj
rand 6 R
also
will be expressed by the relative vectors:
R= - tan'
R7
2 2 YTx >
(2.6)
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. -1lb = - t an (2.7)
IVh ere and pn are the elevation and the azimuth anglesRWWJ ~ y R
of the relative sight line vector in the inertial frame
Then the rate of change of both angles are
Vr9
R R
sine cos;!/ V - sin9 sin^ V + cosG Vv rx v v ryv v rz
(R ~ + R ~ + R )'
~
x y zJ
(2.8)
VTJ
sin^ii V_ v rx v rv
Rcos^ (R " + Ry ) COS
V
(2-9)
Where Vnr and Vn ,are the rates of change in 6 and
Re R-pa v
\p components, is shown in Figure 2.3. These quantities
will be used later in developing the estimator state
equations and will also be used to represent the variables
for the missile seeker. The quantities 6 D , G D ,ib , ip
,K K K K
R and R (corrupted by noise) are the set of measurements
assumed available from the missile seeker.
D. KINEMATICS OF C0NTR01 VECTOR
Figures 2.4 and 2.5 are views from near and side of
miss ile a and <b represent the magnitudes of the
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- K
FIGURE 2.3 RELATIVE VELOCITY COMPONENTS IN POLAR CO-ORD
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DEROLLED-Z-AXIS
MISSILE NORMALFORCE AXIS
a cosem • v
ecos 6to v
DEROLLED Y-AXIS
FIGURE 2.4 KINEMATICS OF CONTROL VECTORS VIEWEDFROM REAR OF MISSILE.
Z-AXIS
a coscbm Y
HORIZONTAL
FIGURE 2.5 KINEMATICS OF CONTROL VECTORS VIEWEDFROM SIDE OF MISSILE.
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missile acceleration due to the acceleration command (ac)
and the bank angle due to the roll rate command (Pc) each
other. g represents the gravitational effect at the center
of gravity of the missile. As mentioned in control method,
the sign of a is such that it is positive upward with
respect to the wings. Figure 2.5 shows that the accelera-
tion normal to the velocity vector has the components due
to the elevator control input and the effect of the gravity.
The total magnitude of the acceleration normal to the
velocity vector can be represented as
a -cos6m Y - ^*sin6
The velocity and acceleration components for simple
circular motion can be represented as
an
- V-/P
See Reference 1.
Using this definition, the differential equations
describing the rate of change of the flight path angles are
a cos >i - gcos6m v ° v
Vm(2.10)
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a si nilmV c o s 3
III V(2.11)
Note am ft cosu) - g*cosG is the magnitude of the normal° v
acceleration component to the velocity vector in missile
side plane and am*sincj) is the magnitude of the normal
acceleration component to the velocity vector in the x-y
plane. The acceleration component of the missile velocity
V has only the component of the effect of gravitv in the
missile side plane under the assumption that the missile
velocity is constant. The differential equation is:
V.m g*sm6v
(2.12)
The rates of change of the missile position components
(Xm, Ym, Zm) can be obtained by integrating the components
of missile velocity. From Figure 2.2, the components of
missile velocity (Ymx, Ymy , Vmz) in inertial frame are
given by
(2.15)X = v = V *cos0 -cosUjm mx m v • v
Y = V = V *cos8 *sini|jm my m v r v
; = Vm m;
V *sin6m v
(2.14)
(2.15)
Let the response of the missile to input command in
normal acceleration and roll rate be considered. If a
demand is made on a missile for a transverse acceleration,
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it is initiated by sending a signal to the appropriate con-
trol surface. The missile acceleration a follows the
demanded acceleration a, in a manner which mav bed
characterized by a frequency u (weather cock freouencvl
and a damping factor P . For simplicity, in present
work, only the first order lags with time constant t and> } 6
p
t are assumed, the equation of the first-order system cano l
be represented as follows:
X(t) + l./x*X(t)) = f(t)
Taking the Laplace transformation on has
X(S) = F(S)/(S+1./T)
with zero initial condition. Applying these to the BTT
missile control system.
t *a + a = aam m c
t *P + P = PP c
(2.16)
(2.17)
Rearranging the differential equation
a = (a - a ) /tm c maP = (P - P )/*c
v c . m p
4) = P
(2.18)
(2.19)
(2 .20)
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Where a and P are the acceleration and roll ratec c
commands. Equation (2.20) is by definition. From the
above differential equations the control ratios are
transformed as follows:
a (S) 1
c a
£_ I jJ_ - _ ±_ (2.22)P (S) t S + 1c p
Equations (2.10) through (2.22) constitutes the dynamic
equations describing missile motion in response to the
command inputs ac and Pc . The solution of these
equations provide missile position (Xm , Ym , Zm),
orientation and magnitude of velocity (ib , 9 , Ym) and
orientation and magnitude of control acceleration ( $ , am)
E. CO-ORDINATE TRANSFORMATION
Given that the missile is on guided flight to compute
the magnitudes of the control inputs at any moment, it is
necessary to transform the instantaneous values from the
sensors in the inertial frame to the instantaneous values in
body frame. The transformation from one frame to another
can be accomplished through a transformation matrix. The
transformation matrix wiill be developed.
Define an inertial coordinate system with unit vectors
I, J, K. Also, define a missile body axis coordinate system
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with unit vectors i, j, k. It is desired to find the trans-
formation between the I, J, K system and the i, j, k system
The I, J, K system can be thought to be oriented so
that I points north, J east, and K down. Similarly, the
i, j, k system has i along the longitudinal axis of the
missile (which by assumption is along the velocity vector
Vm), j out the right wing, and k down.
Consider three successive rotations \b , 8 and ± .yv ' v
The \p rotation is about the inertial z axis, and
transforms to an intermediate axis systems i -,, j -, , k -, .
The 6 rotation is about the i, axis and transforms to anv J 1
axis system i? , j ? , k-,. The last rotation § about the
i 9 axis transforms from flight path axes to missile body
axes. As a result, the total transformation from inertial
to body axes is given by
[3Cev][*v ] = [a]
i
j
K
(2.23)
Where A is the total transformation matrix from inertial to
body frame. The elements of the a matrix from Reference 1
are
:
all = cos(e )*cos(ijj )^ v v
al2 = cos ( 9 ) *sin(ilj )v v
al3 = -sin(6 )v
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a21
a::
a25
a51
ao
a55
7 =
s i n ( <j> )* s in (
9
y
s i n (
o
) Ln I Ln I
s i n ( i )'•• c o s (
cos(>) -sin( ; j:-co> mi
V V
cos(cp) s sin(ev ) *sin(
cos(i) -cos
!
cos(.<j>) *sin(
cos(i) *cos(I
.
sin( j>)A sm(!) J
sin[ p) -cos :
T ,. I
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J 1 1 . AN OPTIMAL CONTROLLE R
Although this work is primarily concerned with the
effect of an optimal estimator on the performance of a bank'
to-turn missile, we will first outline the theory of an
optimal controller in this section. The guidance laws
considered here were first developed by Stallard [Ref. 10].
The following is a brief outline of his work.
A. GEOMETRY
Figure 3.1 shows the applicable geometry representing
the Pro
j
ected-Zero- Control miss (PIC miss) distance in the
terminal state. The problem is considered to be three-
dimensional and two major axes system are used:
1. A seeker-oriented axis system for estimation or
measurement, and
2. The three principal axes of the missile for the
control problem,
Xb , Yb , Zb denote the target coordinates relative to the
missile along its principal axes. The body system is repre
sented a time T = bv a set of Eulian angles which
relate it to an earth fixed system. The angle A<J) is the
incremental roll angle from the present missile axes to any
future orientation at time T , shown dashed in Figure 3.1.
The PZC miss represents the total Proj ected- Zero effort -
miss distance and is defined as the miss distance which
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would occur if no further control was exercised on the
missile at T = Ti . It is composed of a Y- components
(PZCY miss) and a Z- component (PZCZ miss). Under the
assumption of constant velocity the X- component (PZCX miss)
is represented by the distance that the missile lias to fly
at any time, since it is not effected by control, it is
not shown in Figure 3.1.
Y, at time t
Yb- I
PZCZ
a A<J> >h.i
at time t
PZC-miss
FIGURE 3.1 COMPONENTS OF PZC, MISS DISTANCE.
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B. SUMMARIZED DESCRIPTION OF AN OPTIMAL CONTROLLER
Given a time- varying linear system of the form,
X = F(t)X + G(tJ U (3.1)
Where X is n-component state vector
U is m-component control vector.
An optimal control is one that minimizes a performance
index made up of a quadratic form in the terminal state
plus an integral form of quadratic forms in the control and
the state:
7 ^_ ^ f_ t = T i^
+5
fT;
T
(XTAX + U
TBU)dt (3.2)
Where the S, and A are positive semidefinite matrix and
B is a positive definite matrix. An appropriate choice of
these matrices must be made to obtain acceptable level of
X(Ti),
(X(t) and U(t) .
The guidance law determined in Reference 8 is of the
form
U (a, , P,C L.
T
With the performance index chosen (the weighting on the
state vector was not considered in Reference 8.
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TiJ =
2< Xb
+ V J
b ^ I Ti+
2(U - U
b )
TB(U - U
b)dt
(3.3)
Where the first term is one-half the required miss distance,
the second term is included in order to eliminate infinite
energy solutions. B is a 2 by 2 positive definite,
symmetric weighting matrix and of the form
1
(3.4)
with 1/B. = (Ti - To)X maximum acceptable value of
2|U.(T)j" . A detailed derivative of the optimal guidance
law is given in Reference 10.
The solutions of the above equation from Reference 8
give the optimal acceleration command at time T between
TO and Ti as
5Tso
3B, + T1 20
T ( >W (3.5)
Where T is the remaining time to 20 until intercept andct q 00 r
M, is the PZC-miss along the missile normal force axis and
the negative si2n is due to definition of the normal
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acceleration as positive in the upward direction. The
optimal roll rate command at time T between TO and i'i
from Reference 8 is given bv
21a T (Mi )v by'
T + 65 Bgo 2
(3.6)
Where M, is the PAC-miss along the wing axis. Definingby o o o
the roll error:
a j - - 1 |bv
A$ = tanItt^" (5.7)
.An alternate form for P can be obtained bv dividingc °
Equation (5.6) by Equation (3.5), then
/a T (T_£° go
+ 5 B
T + 65 B
tan(A<j>) (5.8)
go
Because the solution of P was based on an assumption ofc t
small missile roll excusions, the tanA^ term in
Equation (5.8) can be replaced by A<J) to yield
7a T T + 5 B,)c go _go V_
a2
T5
+ 65 Bc go 2
Acf> (5.9)
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The optimal controller is implemented in the present work
using Equations (3.5), (3.7), and (5.9). In computing th<
control input , T£0
Y and Z -component of PZC-miss and
the elements of the weighting matrix are needed.
C. APPROXIMATING TIME-TO- GO
It is necessary for the Time-To-Go and the Projected-
Miss - Dis tance in missile body axes to be specified at time
T to compute the optimum control input. The Time-To-Go
will be defined at first, then the state variables will be
introduced as means for formulating the necessary equations
The target acceleration components of the state variables
will be modeled since it is assumed that any information
about the target acceleration cannot be obtained from the
active seeker of the missile. Then using this target
acceleration model the algorithm for computing the other
state variables will be developed.
The Time-To-Go until intercept, T- , is required for
computing the control input. Let the time to intercept be
defined as the time to minimum range. Under the assumption
that the relative velocity vectors may be considered
constant at its present value until intercept. T can be
found at time, T , as
:
T. =l
TR V—r —
r
(3.10)
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Where R is the present position of the target relative to
the missile and the factor in brackets in the component of
V along R . An alternate form of T is:
R V + R V + R VT —__
X rXj_
9 ? 9V+V+V„ (3.11)rx rv rz ' ^ J
The negative sign comes from the sign of V as shown in
Figure 2.2 and Equation (2.6).
D. PROJECTED- ZERO- CONTROL MISS (PZC MISS) DISTANCE
The Proj ected- Zero-Control miss distance (PZC-miss) is
defined as the miss distance which would occur if there
were no further control input after time T .
In this work the state vectors are defined as the
following state variables
X = (R , R , R , V , V , V , a. , a^ t sl. )
T— v x y z rx ry r: tx ty t z
J
Where R is the relative missile distance to targetr °
V is the relative missile velocity to target
a is the target acceleration
A model for target acceleration has been assumed. This
is given as follows:
at(T) = a
tQe" a
'
T|a > (5.12)
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Where a is the intial target acceleration and '- is the
reciprocal of the acceleration time constant. The initial
target acceleration, 4g's, will be assumed for each
scenario, and a = 1/20 for evasive maneuver as indicated
in Reference 10 will be chosen. These values will be used
in the simulation of the optimal controller.
With the assumption that a state estimator is available
to provide estimates of the state vector at current time (tj,
the values of the state variables for no control inputs at
future time, can be obtained easilv using the integration,
i.e.,
\ (t) = Vr(t0) + j* a
t(T)dx
t
to
(t) = R(t0) + f V fx)dTto l
The results of these integrations are
V (t) = V (tO) + l/o, (1 - e" a(t t0)
)at(t0) (3.15)
R(t) = R(t0) + (t - tO)*V (to)
+ [1/a (a(t - tO) - 1 + e
(5.14)
a(t - tO). ,, (tO)
Where tO and t denote current time and future time. It
should be noticed that above integration is based on the
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assumption of a single physical dimension. If this assump-
tion is to be applied to three-dimensional problem, it must
be concluded that there are no coupling among the X, V, Z
dimension. Let T = t - t , then the components of thego o l
PZC-miss in inertial axes at current time (tO) can be
expressed as
"MX
RX
Vrx
My
M7 Ti
Ry
Rz
+ Tg°
TO
Vrv
Vrz TO
fX
fy
f
tx
tv
tz TO
(3.15)
(l/2)*g*Too J
"7 T1
Where f = f = f = l/a"[aT - 1 + e go] and the lastx y z
L go
term accounts for the free fall effect of gravity in the
vertical axis
.
The components of the PZC-miss in missile body axes for
control input calculation are obtained via the Euler trans-
formation matrix:
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Kxl"by
.
Mb^.
[*][e„I[*v ] M)
M
" MX
My
M. z .
(3.16)
Where it is assumed that the missile has exact knowledge of
its orientation angles ijj , 8 , c|> , from the accuratev v
inertial measurement units.
The values of optimal control vectors (a and P J ,r v C C
then, are calculated from time to go (T ) , PZC-miss in° CTO'
o
body axes and weighting matrix components (Bl and B2) which
may be determined according to the desired miss distance and
intercepting time. In a later section, the weighting
matrix will be developed and the scenarios will be set up
for computer simulation.
E. SIMULATION RESULTS
The guidance law in this work was tested on a simple
six-degree-of - freedom simulation using only plausible
numbers without reference to any actual missile or design.
Before the computer simulations, the weighting matrix
of the performance index selected in Reference S will be
calculated using the initial state, X(0) , the missile
operating time imposed, T (5 sec) and the constraint input
vectors. It is assumed that the missile has a velocity
advantage of two over that of the target, a zero- and 0.5
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second-time lag auto pilots for pitch with limits of -3g's,
+20g's and for roll rate with limits of +6.23 rad/sec.
The allowable miss distance which is usually determined
by sizing of the warhead was chosen as 5 meters. Which
makes the first term of the performance index Equation
(3.3) equal to 12.5 meters. The acceleration term and the
roll -rate term in the performance index are set equal to
this respectively (one-half of square mean miss distance),
then
>r (a - a,) ~ dt = 12.5 m'c d
(3.16)
T.
(P ") dt = 12 . 5 m"~ c
(3.17)
The nominal time of flight for the terminal phase is set
equal to 5 seconds. The components of the weighting
matrix B were based on the following assumptions. It is
9
assumed for (4g's) as a mean-square value of (a - a,) and
2(2 rad/sec) as a mean-square roll rate to be acceptable.
Then the components of weighting matrix are:
B1
= 0.003254 sec°
B = 1.25 m"sec
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The commands a and P are computed every 0.05 sec
and are frozen when the Time-To-Go falls below this interval
In order to simulate the optimal controller appropriate
scenarios were developed below with restriction that the
Time-To- Go was 5 sec and the speed of missile is two times
faster than that of target. Three scenarios; tail -chase,
head-on, side- approach engagements, were chosen as possible
cases in this work.
In case 1, tail-chase engagement, the target flies 100m
above the missile and 2500m uprange with a horizontal east-
ward acceleration of 4g's. Figure 3.3 shows the case 2.
In case 5, side- approach engagement, the target flies 400m
above the missile and 1900m uprange with a horizontal north-
ward acceleration of 4g's at t = as shown in Figure 3.4.
For simplicity, when the scenarios were set up, 1000m/ s was
taken as a missile speed, 500m/s as a target speed in case
1 and case 2, 600m/s as a missile speed, 300m/s as a target
speed in case 3, and 4g's as a target acceleration for all
three cases. When simulation was executing, this first
simulation assumed perfect state feedback without the use of
an optimal estimator.
The simulation results are shown in Figure 3.5 through
Figure 5.10. At first, the optimal controller with no time
lag was tested for three scenarios, as shown in Figures 3.5
and 3.6. For scenario 1, the normal acceleration command
in initial phase is very large, then decreases to zero at
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Missile
TO
l
T
4a ' S
aTOe
t/20
Taro.— — * S e t 1 r
I
FIGURE 3.2 SCENARIO- 1 TAIL -CHASE ENGAGEMENT
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T TOt/20
FIGURE 3.3 SCENARIO- 2 HEAD-ON ENGAGEMENT
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J
-K
a <-p
//\^
rr=500m/s
—*<•—
XT= 1900m
'l
Z = 4 010m1
1
L -*-
V =6 00m/ secm
/
/
/
/
//
/
a /- 4 a • saTO/ °
/=
-t/20C* --p / C* i-ri s-^ CT
/
/
FIGURE 3.4 SCENARIO- 3 SIDE- APPROACH ENGAGEMENT
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NO TIME LAG CAS;
o-
oin-
\
WOr
i
LEGEND
A- THIL-CHRSE ENGAGEMENT
O = HEAD-ON ENGAGEMENT
\
\
53
NT
Q.O 1.0 2.0 3.0
Time ( sec4.0 5.0
FIGURE 3-5. N0RMRL ACCELERATION COMMAND VS TIME
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NO TIME LfiG CRSE
oCO"
LEGEND
A- TRIL-CHflSE EN'GRGEMENT
O - HERQ-CN ENGAGEMENT
D- SICE-RPPRCRCH ENGRGEMENT
-3
-11 ! i 1 i ' t t / !
|i r—r—i-
2.0 2.0 4.0 5.0
Time ( sec
)
FIGURE 3-6. ROLL RRTE COMMRND VS TIME
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NO TIME LRG CASE
a.o
LEGEND
A- TRIL-ChnSE ENGflGEMEf
- HEBD-ON ENGAGEMENT
D- SIDE-P.PPROflCH ENGAGEMENT
TLme ( sec
)
FIGURE 3-7. MISSILE BANK ANGLE VS TIME
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at the final time. For scenario 2, the acceleration is z.
in the initial phase, then increases and converges to zero
finally. For scenario 5, the acceleration starts from zero,
but, increases rapidly up to the maximum acceleration then
freezes for seconds before decreasing. The acceleration
does not converges to zero at the final time. Comparing
these results with the variation of the roll rate command
in Figure 3.6, the opposite trends are observed in the
results of scenarios 1 and 2, i.e., if an acceleration
command in the initial phase is large, a roll rate command
is small. The acceleration command in Equation (5.5) is a
linear function of PIC-miss in of z-direction of the missile
coordinate system. For scenario 1, PAC-miss in the
z-direction is relatively large, this causes a large accel-
eration command and a small roll rate command in initial
phase. For scenario 2, the relative magnitude of the PZC-
miss in the z-direction is small. This cause a large roll
rate command in the intial phase. For scenario 5, the
PZOmiss in y-direction is of the same magnitude as the
relative distance as the Time-To-Go decreases. This means
that a larger acceleration command than the maximum value
allowable in the missile body coordinate system is required
at some of instance time during the missile flying. Thus
the acceleration does not converge to zero at the final time
In the derivation of the equations for control commands, a
small angle approximation was assumed. However, in the
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scenario 5 this assumption is questionable. The roll ra
command computed as shown in Figure 5.6 shows a sine wave
characteristics which is possibly due to overcorrection.
The simulation results for a 0.5 second tine lage are
shown in Figure 5.8 and Figure 5.9. The time lag causes
much larger acceleration control commands. This includes
overcorrections, so that the control commands do not
converge in the final phase. The other characteristics
follow the explanations given in the analysis of the cases
with no time lag
.
For all three scenarios the miss distances were obtained
in the simulation of both cases, no- and 0.5 second-time
lag. The miss distances are below 0.5m for all three
scenarios in the case of no-lag. In the no lag case the
control laws gave excellent results. In the lag case, the
miss distances are 5.9m for tail-chase engagement, 0.8m for
head-on engagement, and 52.9m for side-approach engagement.
The miss distance of the side-approach engagement with a
different maximum acceleration limit fo 25g's results in a
miss distance of less than 5m. This higher maximum acceler-
ation limit will be used in the scenario 5 during the simula-
tions for testing the performances of whole control system
in Chapter 5.
The previous results are summarized as follows : (1) the
tracking of a target is very dependent of the missile and
target geometry, (2) the capabilities of the missile
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(maximum acceleartion and maximum roll rate limits an J time
constant) in maneuvering against the target is very important
factors, (3) higher maximum accelerations are required in
any side approach engagement, and (4) the assumption of
small AcJ> in the optimal control law is a limitation in the
general case. Nevertheless, the control laws developed
from the optimal control theory for both the no lag and the
0.5 second-time lag cases yielded successful results for
the specified scenarios. Since the optimal controller with
0.5 second-time lage auto pilots resulted in a miss distance
of approximately 5m with complete state variables feedback,
it is expected that the inclusion of an optimal estimator for
these particular scenarios will results in still larger miss
dis tances
.
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TIME LAG ( .55EC] CRSE
CM
td~
.rfSSSSB^3£E3Ea
J2&
f
Q
!
LEGEND ^A= TF1IL-CHR5E ENGAGEMENT
O - HERD-ON ENGAGEMENT- 3IDE-RPPR0RCH ENGRGEMENT
2.0 3.0
Time ( sec
FIGURE 3-8. NQRMRL RCCELERRTIQN COMMRND VS TIME
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TIME LAG ( .5SEC) CRSE
o
LEGEND
A- TRIL-CHRSE ENGAGEMENT0- HEPC-CN ENGAGEMENT
D- SICE-APPROACH ENGAGEMENT
-3££^^x
a
i
o~1 ' r
3.0
-! 1 r
5.00.0 1.0 2.0 4.0
Time ( sec
)
FIGURE] 3-9. ROLL RATE COMMAND VS TIME
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TIME LfiG ( .55EC] CnSE
o -
o.-r
I
LEGEND
A- TRIL-CHRSE ENGAGEMENT- HERD-GN ENGRGEMENT
D- SIDE-RPPRORCH ENGRGEMENT
0.0 1.0 2.0 3.0
Time ( sec
)
4.0 5.0
FIGURE 3-10. MISSILE BP.NK RNGLE VS TIME
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I V . OPTIMAL ESTIMATOR
A. DESCRIPTION
Without full state variable feedback it is necessary to
estimate the state variables. This chapter describes the
Kalman filters used in the state estimation. These filters
were designed for respectively: (a) two measured states,
(b) four measured states, and (c) six measured states.
All measurements have noise superimposed on them. It is
assumed that the nature of the noise source is known
completely
.
The Kalman filter is the optimal estimator of the
states. Since the measurements are non-linear, it was
necessary to use the extended Kalman filter theory. A
first order extended filter was assumed in this work. In
developing the filter algorithm, the non-linear measurement
equation is linearized about the most recent state estimate
and then the Kalman filter algorithm is applied. This is a
extended Kalman filter algorithm. The results of the
extended Kalman filter will yield suboptimal state estima-
tion .
In practice, the missile will have sensors of various
types that provide inputs which are related in some fashion
to the states to be estimated. The main emphasis in the
present work will be on active seeker which is assumed to
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provide the optimal estimator with measurements of I of-
sight angles for the case of the missile having two measure-
ment sensors. For the case of four measurements one has two
of line-of -sight angles, range and time rate change of
range. For the case of six -measurements one has two of
line- of - sight angles, two of time rate change of the line-
of -sight angles, range and its time rate change. When the
measurement equations and the Jacobean matrix are developed
the six -measurement case will be derived since the other
cases have the same kind but less number of measurement
s ensors
.
We will first consider the state equations and then the
measurement equations. This will be followed by a discus-
sion of the extended Kalman filter. Measurement error
effects on the initialization of the Kalman filter will be
analyzed. Finally, the estimator for the measurement cases
will be simulated in order to test the effects of the imple-
mentation of the different measurment vectors on the
extended Kalman filter.
B. STATE EQUATIONS
The state vector is limited for convenience to the
following state variables:
TX = (R , R , R , V , v , V , a. , a. . a. J- K x ' y ' z rx' ry rz ' tx ' ty' tz ;
(4.1)
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Where X, = R , X_ = R , X_ = R are the components1 X Z y 3 Z
r
relative position, X, = V , X^ = V , X .= V lie com
L 4 rx* 5 ry rz
ponents of relative velocity, and X 7= a«. v , X„
X9
= atz
tx' "8 "ty
the components of target acceleration.
In order to develop the state equations, dynamic equa-
tions of target motion are needed. The target model
selected for tracking applications must be sufficiently
simple to permit ready implementation in weapons system for
which computation time is at a premium yet sufficiently
sophisticated to provide satisfactory tracking accuracy.
To meet these requirements, the model described by Singer
[Ref. 10] will be used in this work. The model will be
presented for a single spatial dimension in order to
enable accurate tracking performance estimates to be made
for a variety of sensor measurement. If the targets under
consideration normally move at constant velocity, the
accelerations due to turns or evasive maneuvers may be
viewed as perturbations upon the constant velocity trajec-
tory. The target acceleration a(t) therefore will be
termed the target maneuver variable, in a single physical
dimension. The target maneuver capability can be satis-
factorily specified by two quantities: the variance or
magnitude of the target maneuver and the time constant,
or duration, of the target maneuver. Hence the target
acceleration, namely, the target maneuver is correlated
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in time. A typical representative model of the target
acceleration as a correlation function, can be express
as follows
:
l (t) = E[at(t)a
t(t + t)] = a
a t
ma > (4.2)
Where them
is the variance of the target acceleration
and a is the reciprocal of the maneuver time constant.
2The variance a ~ of the model will be approximated using
the following formula which represents the acceleration
probability density suggested by Singer [Ref. 10]:
maxm [1 + 4P - P n ]L max J (4.5)
where a is a maximum acceleration rate, P is themax ' max
probability of maneuvering at a , and P n is thev ' & max '
probability of no target maneuvering. And as mention in
previous chapter, a = 1/20 for an evasive maneuver will
be used in this work.
The above process can be modeled as the output of a low
pass filter driven by white noise and it can be described
by the equation
at
= - at
+ W (4.4)
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where a (t) ,the correlation function of the white no
input sat is 1 1 es
•
2(t) = 2ao
25(x)
w ^J m v J 14.5)
The acceleration model above will be applied to each
X, Y, Z dimension with no cross coupling, the system state
equations can be written then as:
X=FX+a + W
000100000000010000!0000 1000000000100000000010000000001
RX
i
Ry
R_
Vrx
V_ry
=
Vrz
?tx
fty
atz
X
y
Rx
Ry
i
R^
Vrx
- amx
Vry
+ " a.ty
+
Vrz
- ayz
a.tx
wX
ty y
K 2
I
w00000000z cz z
I
(4.7)
The Equation (4.7) is a linear state equation with the
assumption that the forcing function a vector, i.e., missile
acceleration, can be precisely measured and also resolved
into X, Y, Z components in the inertial frame.
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Many sensors have a constant data rate, sampling t
data every T seconds. If the above system equation is
represented in discrete equations. The appropriate system
equations of motion may be expressed by
X(K + 1) = :(T)X(K) + B(K) + u(K) 14.8)
where v(T) is the target state transition matrix, B (K)
the deterministic forcing input vector which is composed of
first and second integration and U(K) the inhomogeneous
driving input due to white noise. Since it is assumed that
the missile acceleration is a known deterministic forcing
function, the system equation of motion can be obtained by
direct integration in each single dimension with E|W(t)|=0
The desired integration in discrete form yields
R(tO+T)
V (tO+T)r^
aT(tO+T)
1 T l/ot2(-l + aT + e"
aT) R(tO)
1 l/a(l-e"aT
)
aT
-//tO+T
tOam
(t ^ dtdt
c tO+TJ a„ (t)dtto
m
V (tO)r
at(tO)
X(K + 1) = * (t , a ) X(K) + a (K) (4-9)— mWhere K + 1 = (K + 1)T = t + T, K = KT = tO .
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Next, if the driving input U('t) vector is considered,
this input is not a sampled version of the continuous tii
white noise input W(t) • Since W(t) is white noise,
U(k) also should be a discrete time white noise sequence
that is, E[U(K)U(K+i-)] = for i + . Then as shown
in Reference 10, Li and W are related by
u(k:, CK+1)
J $
KT[K + l)t - t| W(t)dt (4.10)
Where U(K) is a white noise sequence with covariance
matrix
E[U(K)U(K)T
Q (4.11)
Following the above derivation, it can be easily
expanded to the case of three independent dimensions in X,
Y, Z. For a = a = a = a ,and a = a = a = a' x y z x y z
since the model of target acceleration is for one dimension,
the transition matrix is
TI f1
I
I £9 I (4.12)
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Where I and are 5 by 3 identify and null matri
f, = 1/a (aT - 1 .+ e
f\ = l/a(l - e"aT
)
- : r
f- = e-aT
The forcing acceleration vector is
//(K + 1)T
KT KX
(K + 1)TIf a
KTmy
(t)
(t)
(Kr r
l)Tamz
KT
(K + 1)T
KT mx
(K + 1)T
KT my
(t)
(t)
(K + 1)Ta
KTmz
dxdt
dxdt
(t) dxdt
dx
dr
(t) dx
(4.15)
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The covariance matrix of white noise U(K)' is
Q
Qn l Q 12l Q13
I
Q 12I Q 22
I Q25
I
Qi-i Q ?-i Q«i
15 :o
(4.14)
Where
Qn = (a2/a
4) [1 - e"
2aT+ 2aT + (2/3) (aT)
3- 2 (aT)
- 4aTe"aT
]
-aTQ 12
= (o7a ) [e"-li
+ 1 - 2eai
+ 2aTea
- 2aT + (aT)"]
2 2 -2aT -raTQ13
= (a /a ) [1 " e" al
- 2aTeal
]
Q 22= (a/a") [4e
2,2,,, -aT -2aT+ 2j - e . aT]
Q 25= (a
2/a) [e"
2aT+ 1 - 2e"
aT]
Q- = a2[l - e-
2aT]
C. MEASUREMENT EQUATION
In the case of the estimator with six -measurement sen
sors the present study assumes that the active seekers of
missile give the measurements of the sightline angles
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GR , i|>
R , the time rate change of the sight line angles
8 ,, , '<J n , the relative ranae R , and the time rate change
of the relative range R contaminated by the Gaussian white
noise. Then the equations of measurements in discrete time
form are
Z(K) = h(x,K) + Y(K)I 1.15)
h(x)
where Z_(K) is a sequence of measurement vector contaminated
by white noise V(K) and
elevation angle
elevation angle rate
azimuth angle
azimuth angle rate
range
range rate
The missile seekers actually measure the data about the
seeker axes. Thus, throughout this development the assump-
tion is made that the missile possesses an inertial measure
ment unit that accurately specifies the missile's orienta-
tion in space so that a transformation from seeker to
inertial coordinates can be made.
Referring to Figures 2.2 and 2.5, the six-measurement
vectors can be written
3r
q" r
—
r r
R
R
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Rtan
R^
X v
V . r R V + R R Vx z rx v z ry
2 ?
(R + R )Vvx v r:
R (R+R+RUR+Rv x v z ^ v x y
2.1/2
\b = tan+ r
-
Ry
RX
_ m
ru
r R cos
R V + R Vv rx x ry
(R " + R )*• x y
J
R= (R2
+ R2
+ R")*• x y z
R(V
r.R)/R
R V + R V + R Vx rx y ry z r
( R2
+ R2
+ R2
)
l ' Z
K x y zJ
but Rx
= Xx
, Ry
- X2
, Rz
= X- , Vrx
= X4
, Vry
= X. ,
v = x,rz t>
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The measurement vector can be expressed in terms of s
vector
h(x) =
hl
1
h2
h-3
=
h4
h5
h6
tanX,
_____ T_T7
X-Ax-X. + X9
2X_X, +
[ fx-,2
+ X2)X.,X,
1 j 4 2 _ _ ' 1 2 j b} 7 7 •? /? 7 71/9
' xr + V + x3 > <
xi
T X:"J
tanX2
1
X1X2X4
+ X1X2X5
t\ - x2' + x
3
z)
1/z (X^ + X,")
? ?(X - + x/ * x,-)
2 , 1/2
xnx, + x x_ + x_x,14 2 5 _ 6
? ?
(xr + x, + x_-)1/2
(4.16)
The measurement noise covariance matrix for the active
seeker can be written
E[V(K)V(K) T] = R =
a Q
a'2
o
a,2
a*2
a R2
0a65
?
(4.17)
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Where the diagonal elements are the variances of th i di-
vidual measured quantities.
As shown in Equation (4.16), the measurement vectors are
the non-linear functions of state vectors. It is impractical
to implement in non- linear form because the computation of
gain K and error covariance matrix P in update process
in extended Kalman filter algorithm is not possible. To
simplify this computation, and to implement the extended
Kalman filter, the Jacobean or matrix of partial derivatives
H will be determined, i.e.,
3h.
H. •
1"! 9X(4.18)
The Jacobean will be a 6 by 9 matrix because h is a vector
with six elements and X is a vector with nine elements.
Performing the operation, the components of the matrix H
are
H = (-X X ) / (D D )
44 12 1
= (X X )/ IE D )
45 12 1
= H = H46 47 4 E
= (X )/(D )
51 1 C
i
= (X )/(D )
_ *. 2. L
I= (X )/(D
)
53 3 C
= H =049
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OH = rt = J H5 5 5 6 57
j Hbb
3 V: =
61
c 2
63
6 5
66
f (D X ) - (D x )]/ (D )L 4 2 1 z1
J
[ (D x ) - (D x ) ]/ (D )
5 2 2 J
[{D X ) - (DX)]/(D )
k 6 2 3 -J 36
= (X )/(D )
1 C
= (X^)/(D )
(X )/(DJ3 C
K = H = H =067 68 69
2 2 2 1/2C = (X + X f X )
C 1 2 3
2 2 1/2C » (X + X )
1 1 2
2 2 2 2E = X X |(X +X ) (2D D -X -:< (X +X )7(3E *D )
1 1 3 l U 6 01 1 2 5n-J 1 C
.22 2222 > 22E = X X f(X *X ) (2D D -X -X (X + X ) J ( 3 C +C )
2 23^56 012 1 d 6 J 1C.2 2 2 2
= JX (X * X ) X (X + X ) J (D +2D )2K146 25 6 J 1
11( XI>,( D D 1
= ( X X ) / I D D )
12 2 3 1
= ("D )/ (C )
13 1 C
= H = H = H = H = H
1U 15 16 17 13 19=
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5 3h = (E )/(D D )
: i i c 1
5 3
H = ( E ) / ( B D )
2 2 2 C 1
24
(S )/(D D )
3 C 1
2 3
(X X ) / ( D D )13 3 1
(X X )/(D D )
2 3 1
^ *j
27
( D X ) / ( E >
1 3
28=
33
39
2 2
(-X )/ (X + X )
2 1 2
2 2(X )/(X + X )
1 1 2
OH = H34 35
= H = K36 37
=
H =038
,1=
(V /(DC °1 »
i\2
a 3
3 4(D )/(D
nD )
3 2[XXX |X - X j/ (D D )
L1 2 3 4 5 J
1
E = X (X4 2 4
2 2 2 2'X ) f-D D f X (D +2D )1b
L1 11 o J1
= X (X - X ) \-D C + X <D + 2D ")]- 14 5^01 21 J
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D. THE EXTENDED KALMA.N FILTER
Optimal estimators that minimize the estimation error,
can be divided into two processes, the one is the updating
process to estimate the state vector at the current time,
based upon all past measurements, the other is the extra-
polating process to estimate the state at some future time.
Optimal estimation procedure for linear state equations and
non-linear measurement in the form of discrete time will be
described referring to Reference 6. Figure 4.1, a timing
diagram shows the flow of the various quantities involved
in the discrete optimal filter equations.
The discrete system equation whose state at time kt is
denoted by simply X(K) , where U(K) is a zero mean,
white sequence of covariance Q(K) , is
X(K) = $ X(K - 1) , + B(K - 1) + U(K - 1) (4.21)
where is a transition matrix
E[U(K)] (4.22)
E[U(i)U(j)T
] = Q5i;j
(4.23)
The measurements are the non- linear function of the system
state variables, corrupted by uncorrelated white noise of
covariance R(K) . The measurement equation is written as
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where
Z(.K) = h (X,K) + V(K)
E[U(K)] =
E[V(i)V(j)T
] = Reij
(.4.24
(4.25J
(4.26)
The initial conditions are
A AE[X(0)] = X(0) ECCX(O) - Xn )(X(0) - Xn )] = P~
QJ Vii , -0 (4.27)
E(WkVjT
) = uncorrelated (4.2S)
The Kalman filter algorithm is to minimize the estima-
A.tion error, i.e., error covariance. If X denotes an esti
mate of state vector, X , and X is the mean value of the
state vector, the error covariance matrix is defined as
P = E[(X - X)(X - X)1
] (symmetric) (4.29)
The development of the extended Kalman filter algorithm with
a few definitions is essentially more application of the
expectation operator E. We will consider the updating
process then the extrapolating process. The update equa-
tions are used to incorprate the latest measurement in the
estimate and in the covariance. After we obtain the updated
covariance matrix we will then consider the optimum choice
of Kalman gain and derive the equation for the esti-
mation of state. Let X(K-1)(-) denote an estimate of X
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H(K-1),R(K-1)
X(K-1)(-)
v(K-Z),QfK
P(K-1)(-)
X(K-1)(+)
; ( K - 1 ) , Q (' K -1
)
P(K-1)(+)
extrapolat in:
process
H(K) ,R(K)
XfK) !-
)
P(K)(-)
upda
X(K)O)
CK),Q(K
>
P(K)(+)
ting
process
t(K-l)
measurement
process
t(K)
measurement
proces s
FIGURE 4.1 DISCRETE EXTENDED KALMAN FILTER TIMING DIAGRAM
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at time k-1 , given measurements up to and includi .
K-2 , P(K-1)(-) denote the error covariance, K(.K-l)
denote Kalman gain and Hf.lc-1) denote the Jaccobean matrix
at time K-1 . We will seek an optimal observer in the
presence of the state excitation noise and in the presence
of the observation noise.
P(K-1)(+) E{[I - K(K-1)H(K-1)]X(K-1)[X(K-1) T(-) (I-K
(K-1)H(K-1)T)+Y(K-1)
TK(K-1)
T]
+ K(K-1)V(K-1)[X(K-1) (-)T(I-K(K-1)H(K-1)
T)
+ V(K-1)TK(K-1)
T]}
4 .30)
Rearranging Equation (4.50) using the definitions
Equations (4.26) and (4.29) and the assumption that the
measurement errors are uncorrel ated , then one has
P(K-1)(+) = [I-K(K-1)H(K-1)]P(K-1) (-)[I-K(K-1)H(K-1) T]
(4.31)
+ K(K-1)R(K-1)K(K-1) T
This equation updates the error covariance matrix.
Then we still need an optimal gain K. We will choose to
minimize the diagonal elements of the covariance matrix,
i.e.,
J(K-l) = E[X(K-1)( +)
TXV(K-1)(>)] = trace [P(K-l)O)] (4.32)
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mathematical model
s vs tern
—I
u ( K - 1
)
X,
b ( K - 1>
L_.
i x„r+i
1 *k-i
K-lD e 1 a v j
measu remen
JL.
Discrete Kalman filter
+
i(K-l) f+ >
I
Delav >
TBBMK53E3.
.
K
*K(-)
*K-1HK
uo
FIGURE 4.2 SYSTEM MODEL AND DISCRETE KALMAX FILTER.
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Introducing a bit of matrix calculus, one has
~ [trace A B AT
] = 2AB 4.32
aK(K-l)[l-K(K-l)H(K-l) J = -H(K-l)
T(4.34)
Applying these formulas to Equation (4.31) and solving
for K(K-l),
T rK(K-l) - P(K-l) (-)H(K-l)
A
[HfK-l)P(K-l) (-)H(K-l) l
+R(K-l) ]~ 1
(4.55)
This is the Kalman Gain Matrix.
Rearranging Equation (4.31)
P(K-1)C+) = [l-K(K-l)H(K-l)]?(X-l) (-1) (4.36)
Then the update state estimate at time K-l is equal to
the extrapolated state estimate at time K-2 plus a term
which weights the measurement residual via gain K(K-l) .
1((K-1)( +) ='X(K-1)(-) + K(K-l)[Z(K-l)-h(k-l),
/X(K-l)(-))]
(4.37)
We need to consider also the propagation of the estimate
X(+) between measurements and the propagation of the
covariance matrix P(K-1)(+) between measurements. Propa-
gation of the estimate is straight forward and involves only
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HK-1
RK-1 QvK-l
rK-l
-!- I. ,ll. t , ,,,.,- . . , - I — |,
compute P,._ , ( + ) J
EQ ( 4 - 2 7
)
t K
HK
--,-
compute !'r
EQ(4- 25)
Reasonab ! e n e sslChecks
.
..- .j
compute k'
EOT 4- 26)
Update
Estimate
h:'^ x
I:ompute X,, ( + ) i Reasonableness \-»m>
Checks
;'K-1
x r (-)—K
setK
->
h(K)compute X v f -
)
EQf4- 24)
FIGURE 4.3 DISCRETE KALMAN FILTER INFORMATION FLOW DIAGRAM
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the application of the state transition matrix and t]
forcing terms
.
X(K)C-) = I>(K-1)/
X(K-1H + )+ U(K-.l) (4.33)
iy the definition (4.29), solving for P(K)(-)
P(K)(-) = <?(K-1)P(K-1) ( + )i(K-l)T
+ Q(K-l) (4.59)
Figure 4.2 illustrates the above developed equations in
block diagram form which contains a system model, measure-
ment process and the Kalman filter to be implemented, and
Figure 4.5 shows a simplified computer flow diagram of the
discrete Kalman filter.
Equations (4.55) through (4.59) constitute the recursive
formulas for implementing the extended Kalman filter
algorithm. The process is initializing by providing values
x(0)(-) and p(0)(-) . In order for the extended Kalman
filter to be simulated, the next section will describe the
initialization of the Kalman filter.
E. INITIALIZATION OF THE KALMAN FILTER
The one objective of this work is to test the perform-
ance of BTT missile control system having a Kalman filter as
an optimal estimator as a function of different measurement
vectors. All values for initialization of former worker in
this work (Ref. 14) will be used for computer simulation.
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1 • I nit iali zing the Error Cov ari ance Matrix , P
Let a , a and a define the standard deviati np v a
of relative position estimate, of relative velocity esti-
mate, and of target acceleration estimate. The initial
values of error covari ance matrix p , will be chosen as
follows. The first six elements of the initial state
vector are determined by
UO) = X(0) + V(0)
Where the measurement vector X is the true measurement
vector X plus V caused by measurement uncertainties.
The expected mean square of initial covariance matrix
components. With the assumption that the initial
covariance matrix is diagonal with the diagonal elements
reflecting the uncertainties associated with the initial
state vector estimates, the components of the covariance
matrix are
E[V1
] = E[V2
Z] = E[V
5-]
E[V/J = E[V, ] = E[V/]v
The last three elements of the state vector, acceleration
components, are set to zeros
x7
= x8
= x9
-
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2. Initializing the Measurement Noise Variance Matrix R
Measurement errors tor the active seeker
assumed to be due to thermal noise, gimbal angle
error, environmental noise, and glint. The values of one
sigma errors in each measurement will be used in present
study. Because in the case of angle measurement, the envi-
ronmental noise is proportional to the square of range to
target and glint errors the wander of the apparent target
centroid as seen by the seeker as varing inversely with
range to target. The errors caused bv noise are generallv
a highly complex function of the target geometry, target
radar cross section, radar receiver, signal-to-noise ratio
and etc. For simplicity, in present study, the constant one
sigma errors (average values) in each measured vector are
assumed to initialize the Kalman filter. Referring to
Reference 15, the one sigma values of the average errors
for measurement of angle, angle rate, range and range rate
of the present active seeker are tabulated below.
(1) angle measurement errors
(2) angle rate measurement errors
(5) range measurement errors
(4) range rate measurement errors
Before the simulation runs is undertaken, it will be
briefly discussed the manner in which the Kalman filter on
board the missile is initialized at the start of the
0.15 -0 .6 deg
0.5 -2.0 deg/sec
5 meters
6 m/sec
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engagement based on sensor data from the launch aircra :t
.
The launch aircraft's sensors are assumed capable of leter-
mining , within some prescribed accuracy, the relative
position and relative velocity of the target with respect
to the missile at the instant of launch. This information
is sued to initialize the first six elements of the filter's
state vector. However, the launch aircraft is assumed to
be able to provide no information regarding target
acceleration. Thus the three elements of the initial state
vector are set to zero, finally, the complete list of
nominal parameters for initialization is provided in Table
4.1.
F. EVALUATION OF THE OPTIMAL ESTIMATOR PERFORMANCE
The performance of the state estimator with two-
measurement vectors (case 1) , with four-measurement vectors
(case 2) , and with six-measurement vectors (case 5) , was
simulated to evaluate the effect of the possible implementa-
tion of the more measurement sensors on the bank-to-turn
missile for typical scenario (tail chase engagement case).
The error covariance and the Kalman gain components
selected, and the states estimated, were computed as shown
in Figure 4.4 through Figure 4.30. The control laws imple-
mented with the estimator also were tested to evaluate the
performance of the control system of the BTT missile as
shown in Figures 4.51 through 4.55.
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TABLE 4-1
NOMINAL PARAMETERS
3 s cn c
2 3 r -
3 cr.s :
: :
-
T j • 3
a6'
Gq 1 . 1
e,u4 J . 5 1 : :/ .
•
; . 5
3 J. -. .-.
]
t r r
d or. : s i g m = s j.a r.t
ifir. 5-: r i '. •. • i. j. - _
6 n i s s
1
1 s i c 1 1 rats
risna ccr. start
- i r " e en s t a r.t
vi:- i =si 1 ;
- oraxi rc urn
normal 2cc.
9 missile iraxisnua)
re II rats
10 filler frocess n c is
p a ra in = t = i
a target r m s ace.
c tare?-: .-nan uver
tan d width
11 ccnticll^r cost
function d a r ?. sir- 1 ' :
T -. .{
a
o . m / s c
.) . 5
J . 3 o C
20 7' s
0.3 - 2.0
z .
6.23 r a a /s 5 c 1 - 1
^ g ' s
1/2 s~c
i «;nq:.t en a
o n
0.003254 si
e
1 . 2 -j 7. 5 :• c
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Figures 4.4 to 4.12 represent the variations of t
error covariances of each state variable. Figures 4.
through 4.7 show the three covariances for the relative
eositions. i.e., P, n , P~ , P,, . Only the covariance' 1122 33
of the two measurement case varies substantially. Thus
one would expect that the gain components G-, -, , G?
, , G--.
would show only variations in the two measurement case as
are shown in Figures 4.15 throueh 4.15. The trend noted in
the set of graphs (4.4, 4.5, 4.5) and in the corresponding
Kalman gains is also observed in the other covariance
components. Thus the covariance components for the velocity
(Figures 4.7, 4.S and 4.9) should larger variation is
reflecting in the corresponding Kalman gains of 4-16, 4-17,
4-18. The covariances of the acceleration components show
a similar development but there appears to be less variation
in a. and a„_ acceleration covariances. In the case ofty tz
a t the variation of the Kalman oains are restricted to atz °
much narrower range. The error covariance and the Kalman
gain components in all three cases eventually converge to
very small values. Figure 4.22 through Figure 4.30 show the
results of the state estimations of the three estimators.
Figures 4.22, 4.25 and 4.24 show the variations of the
estimated relative position (R , R , R ). Figures 4.25,
4.26 and 4.27, the variations of the estimated relative
velocity (V ,, V , V ). Figures 4.28, 4.29 and 4.50 theJ rx ' rv j-z
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variations of the estimated target acceleration (a. ,
a , a ) . The trends of the curves representing the
estimated states are similar except for Figure 4.25 which
shows a wide variation in the estimated V component ofi A.
the twr o measurement case from the true state values.
Generally, the estimated states of the estimator with
two- and four-measurement are close to the true states,
in spite of the wide variation of error covariance compo-
nents of two-measurement case. The estimator with six-
measurement vectors generates the estimated states which
show almost same characteristics as the estimator with
four-measurement vectors, but usually underestimated the
states in comparison with the generated states of the
estimator with four-measurement vectors. Figure 4.32 and
Figure 4.54 show the curves of the control commands com-
puted using the estimated states. The results of the six-
measurement case have a wholly different form from the
curves representing the computed control commands in the
other cases. The normal acceleration commands computed
using the estimted states with the s ix -measurement vectors
initially have smaller values, but finally reach maximum
value as shown in Figure 4.52. Also as shown in Figure
4.54, the roll rate commands run from the minimum limit to
the maximum limit. The large variations of the both control
commands of case 5 during a short time interval result in
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IRIL-CMRSl ENGAGEMENT CRSE
LEGEND
A - 2-MEHSUREMENT VECTOR CRSE
D - 4-MEfiSUREMENT VECTOR CRSE
O - B-NERSUREMENT VECTOR CRSE
Q.O 1.0 2.0 3.0
Time ( sec 1
4.0 5.0
FIGURE 4-4. ERROR GQVRRIRNCE COMPONENT CP n '
VS TIME
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TRIL-CHRSE ENGAGEMENT CR:
CO
o -i
a-
CM
Q_
ce: lo-
ci;-
en
o
DLO'
o -1
a
LEGEND
A - 2-MEflSUREMENT VECTOR CASE
D - 4-MEflSUREMEMT VECTOR CfiSE
O - S-MEFISUREMENT VECTOR CfiSE A *\
A \A \i k
t i1 1
1 r
a. a 1.0
—j—i—i—i—i—|—i—i—-—i—
j
2.0 3.0 4.0
Time ( sec J
5.0
FIGURE 4-5. ERROR COVRRIflNCE COMPONENT (P 22'
VS TIME
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7RIL-CHRSE ENGAGEMENT Cf
a
LEGEND
A - 2-MERSUREMENT VECTOR CASE
- 4-MERSUREMENT VECTOR CRSE #\O - 6-MERSUREMENT VECTOR CRSE / \
i r1 1 1
i i 11 r I r
2,0 3.0 4.0
Time ( sec 1
zizz^mzcB-5.0
FIGURE 4-5. ERROR CQVRRIRNCE COMPONENT tP^]
86 VS TIME
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TRIL-CHR5E ENGAGEMENT OR:
LEj'ENO
A - 2-MERSUREMENT VECTOR CRSE
CTGR CrSE
O - B-MEHSUREMENT VECTOR CRSE
~i 1 1 1
1
1 1 1 r
1
1 1 1 1
1
r
CO 1.0 2.0 3.0
XLme
(
sec
4.0 5.0
FIGURE 4-7. ERROR CQVRRIflNCE COMPONENTS,
VS TIME
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TAIL-CHASE ENGAGEMENT CRSE
CD-t
LEGEND
A - 2-:-'L"SUREMEN7 VECTOR CRSE
„V\.
-MEASUREMENT VECTOR CRSE J?\
2.0 3.0
Time ( sec
]
4.0
FIGURE 4-3. ERROR COVRRIflNCE COMPONENT
88
55J
VS TIME
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TRIL-CHR5E ENGAGEMENT CflSt
aa.
LEGEND
A - 2-flER-SUREMENT VECTOR CRSE
D - ^-MEASUREMENT VECTOR CRSE
G - 5-MERSUREMENT VECTOR CRSE
~»1 r-
4.0
-i 1
1
1 r
5.0q.o 1.0 2.G 3.0
Tims ( sec
]
FIGURE 4-9. ERROR GOVRRIRNCE CQMPQNENTC' 66
J
VS TIME
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THIL-CHRSl ENGAGEMENT CRS1
LEGEND
A - 2-MEflSUREKENT VECTOR CF.EE
C - ^-MEASUREMENT VECTCR CR5E
O - 6-MEflSUREMENT VECTOR CASE
FIGURE 4-10. ERROR CQVRRIflNCE COMPONENT (P77
VS TIME
90
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TRIL-CrlfiSE ENGRGEMENT CH3E
LEGEND
A - 2-MERSUREMENT VECTOR CHSE
C - 4-MEfiSUREMENT VECTOR Zrz:
C = 6-MEnSUREKENT VECTOR CH3:
FIGURE 4-11. ERROR CQVRRIRNCE COMPONENT (P^)
V5 TIME
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TRIL-CHR5E ENGAGEMENT CRSE
\
a-U3
enen
Q_
E—
CDen
a
CD
a.
LEGEND
A - 2-MEflSUREMENT VECTOR CRSE
D - 4-nEflSUREriENT VECTOR CH5E
—
|
1 I I 1
1
:1 1 1
1
1 1 1 1
1
1 1
2.0 3.0 4.0 5.
Q
Time ( sec
)
0.0 1.0
FIGURE 4-12. ERROR C0VRRIRNCE COMPONENT (Pgg
)
VS TIME
92
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IRIL-CHflSE ENGAGEMENT OR:
a 4
LEGEND
A - 2-MERSUREKENT VECTOR CRS;
- 4-MERSUREMENT VECTOR CASE
- 6-MERSUREMENT 'ItlZTZR CRSc
FIGURE 4-13. KRLMRN GRIN COMPONENT ( G, 1) V5 TIME
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TRIL-CHF13E ENGRGEMENT CRSE
oCM
aCO
l:oe:;e
a - 2-mersurement vector cpsi
- 4-mersurement vector crse
o - 6-mersurement vector crse
A
,*^~AsEHnssss^EE rsHis^r^'rrerrrrr—rcr
(XI
C3 3 '
a 1
aCO-CO
aoCO
I
a.
a
1.0 2.0
Time ( sec
A
—,1 1 1 1
11 1 1 r—|
r
3.0 1.0 5.0
FIGURE; 4-14. KRLMRN GRIN COMPONENT (G21
) VS TIME
94
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ItiIL-CHRSl ENGRGEMENT CH5;
aa
er.C3in
LEGEND
A - 2-MERSUREMENT VECTOR CRSE
D - 4-MEHSUREMENT VECTOR CASE
O - 6-MERSUREMENT VECTOR CRSE h£ &
a.o 1.0
T 1 1 1 p2.0 3.0 4.
TLme( sec 1
7r
5.0
FIGURE 4-15. KRLMflN GRIN COMPONENT (
G
311 VS TIME
95
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TfllL-CHRSE ENGAGEMENT CRSE
a c00
ato'
LEGEND
A - 2- N!ERSiJRE*ZNT VECTOR CRSE
D - 4-MERSUREMENT VECTOR CRSE
O - 6-MEflSUREKENT VECTOR CRSE
a.
a
-i 1 1 1]
1 1 1 11 i r—i r
1.0 2.0 3.0 4.0
Time ( sec 1
i—'—
'
5.0
FIGURE 4-16. KflLMflN GRIN COMPONENT (
G
41) VS TIME
96
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TRIL-CHR5E ENGAGEMENT CnSE
m 6
CD
oain
oo-ai
oO -I
inrv
LEGEND
- 4-flEHSUREMENT VECTOR CP.SE
- 6-MERSUREMENT VECTOR CASE
i
a.
a
L.O 2.0 3.0
Tome ( sac
4.0 5.0
FIGURE 4-17. KRLMRN GRIN COMPONENT (G51
J VS TIME
97
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TAIL-CHASE rwcfi
i T
LEGEND
A - 2~MEftSUREf1ENT VECTOR CASE
- i-MERSUREMENT VECTOR CRSE
O - 6-MEHSUREMENT VECTOR CRSE
CO 1.0 2.0 3.0
Time ( sec J
4.0 5.0
FIGURE 4-L8. KALMAN GAIN COMPONENT [GB1
3 VS TIME
98
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iril-chrse engagement crss
aCM
I
oa
a - 2-MEflsUREMENT vector chse '
D - 4-MERSURET1ENT VECTOR CRSE
O - 6-MERSUROIENT VECTOR CRSE
i r r r -r 1 1 1 11
r
a.
a
1.0 2.0 3.0
TLme ( sec
4.0 5.0
FIGURE 4-19. KRLMflN GRIN CGriP ia71
V5 TIME
99
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en nlC3
GD'CD
oo
LEGEND
A - 2-tfEfiSUREMENT VECTOR CfiSE
D - 4-flERSUREMENT VECTOR C,g5l V
G - 6-flEHSUREMENT VECTOR CRSE V/*
i1—'"
a.a
—t
1
r } r——
•
r 1r
1r r f r*
1.0 2.0 3.0
Tims
(
sgc )
-i—|—i—
-
5.04.0
riGURE 4-20. KRLMfiN GAIN COMPONENT (
G
fl1] V5 TIME
100
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TKTI -PUOCr PTMPRPF'MP\IT HOC!iniL Urtnou LlNbnbQllLiNi Oho!
:^%
' r- p >i n
A - 2-MERSUREMENT VECTOR CASE
D - 4-tlEHSUREPOT VECTOR CfiSE
O - 6-MEP.SUREMENT VECTOR CASE
-i 1 1 1 1 11 r i
1 1 r -i 1 11
5.02.0 3.0
Tome I sec
]
4.0
FIGURE 4-21. KRLMRN GRIN COMPONENT (
G
gi) VS TIME
101
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the worst miss distance, 15.4m. The control commands he
other cases behave almost like characteristics repres
that the control commands are increasing smoothly :
>
zero to its maximum control command, after two or three
seconds then slowly decreasing to converging values. The
relatively small variations of the both control commands
during a short time interval comparing with the control
commands of case 5 result in 9.7, miss distance in case 1
and 8.7m, miss distance in case 2. From above results, the
estimator with four-measurement vectors gives the best
result, the estimator with six-measurement vectors the
worst result and the estimator with two-measurement vectors
almost same result as the of four-measurement case comparing
with the result of the estimator with six-measurement vectors
In original measurement equation, for case 1, the
missile has two-measurement vectors which give the informa-
tion about the relative angles, 6 n and \b n , was assumed.& ' R R
These two -measurement vectors are non- linear functions of
three relative distance components, X-,, X_ , X_ . For case 2,
the missile has four-measurement vectors which give the
information about above two angles plus relative range and
time rate change of relative range which are also non- linear
functions of the components of six state vectors, X-, , X-,,
X, , X,, X r and X, . For case 3, eventhough the missile3 4 5 6
has six-measurement sensors are assumed, the measured
102
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TRIL-CMRSl engagement crse
om 'ag.00
-̂ -.
-~f-.- ---
- -
S**i• -.-.
- -I
-^•*-I- -IO -
- hf-i
O -1 ^CD - '~t
oo-^
a
CO
CD
©
,Q 4
Oa
om
od.
LEGEND
+ - TRUE Rx
A- ESTIMATED RX12:
C- ESTIMATED Rx(4:
G - ESTIMATED \:S
*—-* T"t
n r r-
Q.a 1.0 2.0 3.0
Time ( sec
)
4.0 5.0
FIGURE 4-22. STRTE ESTIMRTE: RxVS TIME
10
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IRIL-CHRSE ENGRGEMENT CR3E
-rv* rt
2^wb4^
:j
^̂ 4\ a
Gk + Gj
. 7?iRv
A - ESTIMATED RY (2)
D- ESTIMATED RY(4)
O - ESTIMATED RyCS)
©©
2.0 3.0
TLme ( sec
]
4.0 5.
a
FIGURE 4-23. SIHIl E5TIMRTE: RyVS TIME
104
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TRIL-C^ C SE ENGRGEMENT CR5E
c —CO
oa'
o
CO
LCD
CD
Q^
I
LEGEND
A- ESTIMRTED Rr (2)
D - EJ37r^F.TEID R (-)
- ESTIMATED rO:S;
jmgmPMA,
24? ^sg^
iiiiiii ii iuii
CJCM
I i i r
a.
a
L.a 2.0 3.0
TLme ( sec
4.0 5.0
FIGURE 4-24. STRTE ESTIMATE: RzVS TIME
10 5
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TRIL-CHRSE ENGAGEMENT CR!
2.0 3.0
TLme ( sec
FIGURE 4-25. STATE ESTIMRTE; VRX
75 TIME
106
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IRIL.-CHRSE ENGRGEMENT Ci
R"1
CD-
C3 H
a j
4T -
MX2 -
to
co gff Ac -i A rreP
£=4'
COI
mi
C3 —
|
I
° 1
\ 1
% %V«L
tv
%
LEGEND4-- TRUE V
Ry
A- EISTlr-TETD VRr C2)
n- E5T: Mn7Ej 7^(4)
O- ESTIMATED Vov (SJ
irtTtS
4-
a?/
-i^oj
~~1—'—'—'—'—1—'—'
—
'—'—1
—
'
2.0 3.0 4.0
TLme ( sec
)
0.0 1.0 5.0
RIGURE 4-25. S7RIE ESTIMRTE: VRY
VS TINE
107
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TRIL-CHRSE ENGAGEMENT C^zi.
tv.
o —
LCtoLNU
+ - TRUE VRZ
a - estimated v^ 2
D - ESTIMATED 4
- ESTIMATED V'RZ
5
-r-—
i
1 r-
1.0 2.0 3.0
Time [ sec
.0
FIGURE 4-27. STATE ESTIMRTE: V 9T VS TI
108
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inlL~uhh;Dt tiNbribQriLiNi unbL
^3Si
3.0
ILme i sec
)
FIGURE 4-28. STRTE ESTIMRTE:: au VS TIMI
109
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IRIL-CHnSE ENGRGEMENT C
a -
onmi
o-^ J
i
_r
—
r
j —
i
:
t—'—I—
Q.Q 1-0
O - E2TIMRTEH all 6
3
~1—'—'—'—'—i—'
—
r
2.0 3.0
Time ( sec
)
-t—i—|
—
t •i
5.04.0
FIGURE 4-29. SIRTE ESTIMATE: an V5 TIME
110
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IrliL LnhbL LiNbnbLl iLlNl brio!
LEGENDi <r
A - ESTIMATED 0^(2]- ESTIMATED o^M)
C - ESTIMRTED o~,i5;
2.0 3.0
Time ( sec
FIGURl 4-3G. sthtt TIMRTE: Gtz VS TIME
111
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TRIL-CHR3E ENGAGEMENT Gn:
A -
-
O -
LEGEND
true: time-to-go
estimated time-t0-g0(2)
estimated time-t0-gch4]
estimated time-ta-gq(6j
2.0 3.0
Time ( sec
)
FIGURE 4-31. ESTIMRTED TIME-TQ-GQ VS TIME
112
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TRIL-GHRS: ENGAGEMENT GnSI
(0
CDQ-
uv
LEGEND
A - 2- s"E."SU-;rE
MENT CP5E!
- 4-MEflSUREMENT CRSE
O - B-MEnSURENENT CRSE
r^=gi
<£V?ibd
/ |L/\4
I f
d
5
fam .
aa. .
ad. 1
1r—r——
i
[
11 1 1 : 1 1 p
p-—
j
1 r-
Q.O 1.0 2.0 3.0
TLme ( sec
]
1.0 5.0
FIGURG 4-32. NGRKfiL RCGGLERnllGN COMMAND VS TIME
113
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lniL~LnnbL :NGflGD1E:NT CF I JL_,
(M
OL.O-
00 acr,^-*
£
ain'
LEGEND
A - 2-NERSUREMENT 2"5^
D - 4-MEF15UREMENT CRSE
- B-MEaSUREMENT CRSE
rg^gS*5
a. ^13—i
1 1i 1
1 1 1 1]
1' ' '
1 1 r-
Q.C 1.0 2.0 3.0
Time ( sec J
—I1 1
1
1
i [ 1r
4.0 5.0
FIGURE 4-33. MISSILE NORMAL LOADING VS TIME
114
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tril-chrse: engrgcment cr:
LE3ENQ
C - 4-MERSUREMENT CRSE
2.0 3.0
TLme ( sec
FIGURE 4-34. ROLL RRIE: CQMMRND VS Til
115
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TRIL-CKRSE ENGRGEMENT CRSS
LEGEND
a - 2-mef1surement crse
- 4-mersljre v e:n7 case
o - 6-mefisurement crse
1
a .
ft
^^Esjgf] A
a.
a
L.O 2.0 3.0
Time ( sec
]
4.0 5.0
FIGURE 4-35. MISSILE ROLL RAIL VS TIME
116
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tqti -puqcj: FNQflRFMFMT pci n 1 1_. ijnnjL i . in •.:nul i il. i n i uno c.
LEGEND
n - 4—MERSI REMENT CP c:r
q „ g-f-lEfisuREMENT CP.S"
iume l sec J
FIGURE 4-35. MISSILE BfiNK RNGLE VS TIME
117
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vectors using the six-measurement sensors contain
state vectors which are exactly same kinds and n u
the measurable state vectors using the four measure
sensors in case 2. As the angle rates, 5} ^ 5
included to the estimator as the measurement vectors in
case 5, the both angle rates are non-linear functions of
only six-state vectors, X., X , X-., X , X- and X .
The results of the estimator with four-measurement vectors
and with six-measurement vectors show the almost same
variations of the error covariance and the Kalman gain com-
ponents of the six measurement case as of the four measure-
ment case as shown in Figures 4.4 through 4.21.
The Kalman filter in this work is a first order filter.
Thus the components of the Jacobean matrix contain only the
first order term, i.e., all higher order terms are neglected
As the order of the system is increased one would expect a
more complex system to response differently. The above
reasons affect to the underestimation of the states of six
measurement case as shown in Figures 4.22 through 4.30.
From the above considerations it can be concluded that as
the increased number of measurement vectors is implemented
to the extended Kalman filter algorithm, the result of the
system may not be enhanced up to the expected degree due to
the increased complexity of the system. The system with the
118
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first order filter as a optimal estimator may con
to make less desirable results due ro the effec / .
lectins of the higher order terms in the filter alsoritl
119
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v - PERFORMAN C E H VA LUATION OF THE CONTROL 'EM
This section will discuss the performance of t le control
system implemented in a bank-to-turn missile for each scen-
ario. The maximum normal acceleration, the maximum roll
rate and the time constant are the key parameters which
reflect the missile capabilities, the noises to measured
vectors are the parameters of the environmental effect, and
auxiliary variables of some importance are then total engage-
ment time and missile velocity. The mean miss distance
determined from the 50 Monti Carlo runs was used as a
performance standard for the estimators of tuo- (case 1) ,
four- (case 2) and six-measurement vectors (case 3) on each
scenario. The simulation results are shown in Figures 5.1
through 5.19. The sensitivities to variations of the para-
meters will be discussed below. Since the simulation results
show essentially the same characteristics for the tail -chase
engagement (scenario 1) and for the head-on engagement
(scenario 2). The performance on these two scenarios will
be discussed together for each parameter. The side-
approach engagement (scenario 5) will be discussed sepa-
rately.
Figures 5.1 and 5.2 show the miss distance variation as
a function of missile maximum normal acceleration. For
scenario 1, case 1 shows a mean miss distance of 9 . Sm at
120
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o J2
s =
CD
"
-lJ
CO
._>
en £
OH
oo
5.0
:^IL-CHR5l engagement cf .
x=0 .05 { 1/ssc ) aR=3(m] CT&=6(m/sec
= o- =. 1 5 ( deq ] =0.5 sec;
a. "a," . D i jeq/ssc ; '^vc-^S.l:9 -J i IhA
Enaaqement Tlme=5(sec
Missile Velocity = 100G (rn/sec)
LE3END
A= 2-MEflSUREMENT VEC1 i
a = 4- m e=sl:r: v e:>
;: vectors
O - 6-MERSUREMENT VECTORS
10.0 15.0
aaox
l 9 S
20.0 25.0
FIGURE 5-1 MISSILE MAXIMUM ACCELERATION VS
MEAN MISS DISTANCE
121
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"J!
%i
£ =>
CD
0)
c
*JCO
. J>
OCO •
CO°
CO
o
10.0
:rd-on engagement case
x-0.05(l/sec) crR=3(m] cr£=6(m/sec
=.15 (dea) t=0.5(sec)
= cr,= . b ( deq/sec )
1flX= 6 . 28 ( red/sac
)
LnGccement iLme=^lsecJ
MLssLLe VeLocLtu = IOCS (^/sac
LEGEND
A = 2-MEflSUREMENT VECTORS
n - 4-^E c SE c::-'E',: vectors
O = 6-MEflSUREMENT VECTORS
15.0 20.0 25.0
°.«<S'«'
FIGURE 5-2. MISSILE MRXIMUM ACCELERATION VS
MEAN MISS: DISTANCE
122
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above log's, case 2.9m at above 15g's and case 5, L5.
above 15g's. Each case also shows that as ti axi tit
of the normal acceleration decreases, the mean ;
:
in case 1 is Jess sensitive than in the other two cases.
For scenario 2, the result of case 1 shows the minimum mean
miss distance is 10m and the result of case 2 is 17m at
a = 21cr r
s respectively, the result of case 3 is 52m atmax a v ' '
a = 17g's. The trends of the curves show that the meanm ax ^
miss distances in case 1 and case 2 vary in same manner,
which rises sharply as the maximum acceleration decreases.
On the other hand, the mean miss distances in case 5, vary
smoothlv above a = 13g's . One may conclude from themax °
analysis as follows: for scenario 1, the mean miss distance
can be decreased since more information on the state vectors
can be obtained from the increased measurement vectors in
estimator for low maximum acceleration. In order to get the
mean miss distance below 20m for all three cases, one needs
a of over llg's. For scenario 2, as the measurement statemax ° '
vectors are increased in estimator, the mean miss distance
does not decrease, possibly due to the effect of neglecting
the higher order terms in the extended filter. As the mag-
nitude of the values of the missile and target geometry
components becomes larger, the effect of the linearization
becomes more pronounced. a = over 19g's is required tov max ° 1
123
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to obtain the mean miss distance below 15m in case 1 a
case 2. In case 5, the mean miss distance does not
below 20m.
Figures 5.5 and 5.4 the variations of the mean miss
distances as missile maximum roil rate varies. [n scenario
1, for case 1 and case 2, a mean miss distance of below 10m
can be obtained when P is greater than 5 rad/sec.m a x °
case 5, one has a mean miss distance of below 16m with
P greater than 6 rad/sec. The trend of the mean missmax to
distances is almost constant over certain range in values
of P . The variation of the mean miss distance belowmax
this range is a tenth of the magnitude comparing with the
variation of the mean miss distances as a function of
maximum acceleration. It is concluded that for scenario 1,
the mean miss distance in case 1 and case 2 is not effected
by the roll rate if the roll rate is above 5 rad/sec. In
case 5, higher roll rate limit can reduce the mean miss
distance. For scenario 2, the mean miss distances of case
1 and case 2 are like the trends for scenario 1. However,
the mean miss distance of case 5 has a minimum of 26m at
P =4 rad/sec, then increases up to 36m then decreasesmax ' r
slightly. This trend ma}- be due to the effect of the roll
rate lag, which is caused by the increased maximum roll
rate and by the underest imat ions of the state variables in
case 3. The other characteristics, i.e., the mean miss
distance decreases with increased maximum roll rate limit
124
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TriIL-GH-51 ENG :NT Cr.5l
CD
o-
£ a'— -
0)
acc*}en
.->Q
ca"
x-0.05(l/SEC) crR-3(m) o-
R=6(m/sec)
c =ct =. 15 (dec ) "C
=Q .5 (sec)9 3
a ,=.5(daa/3ec) ^ qy=^U lq s J
Encaaement TLme*"5(sec)
Missile VeLocLtu = 1000(m/sec
legend
£ = 2-^ER5URE^iNT VECTORS- 4-MERSUREMENT VECTORS
0= 6-MERSUREMENT /ECT
-] 1 1 11
[
1 1 1 1
1
1 1 1 1
1
1 1 1•' ,
•:
11
1
1
0.0 2.0 4.0 5.0 8.0 10.0
P (rad/sec)max
FIGURE 5-3. MISSILE MAXIMUM ROLL RnTE VS
MEAN MISS DISTANCE
125
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HERD-ON ENGRGEMENT CRSJ
CD
CH
(D
O
CO
W 'I
CO S
aC3-I
x-0.05( 1/SEC) c- D=3[m] o-p-5(m/s8c
».-*•" lo( sec
rpx= ^u Lq s J
Enaaqemeni Tlme=*5(sec]
MLssLle VeLocltu - lOCGlm/sec)
-A-
LEGEND
A - 2-M I=5urtc:Mc::iT /ECTGRS— (i.MracjipfMF; — UPPTP, 1? ;l_l t i I Li i^J[\u iCiN 1 < i iL _O - 5-MEnSUREflENT VECTCRS
•A- -A
:.0 5.0
pM>cd/sec]a.t 10.
o
figure 5-4. missile: mrximum ROLL RRTE vs
MEAN MISS DISTANCE
126
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and for scenario ! , case 2 shows less near miss di lc
than case Z, but for scenario 2, the opposite tren s,
can be explained with the same reasons as those of Eirst
ana Is /is
.
The variation o f the mean miss distances with the time
cosntant as a parameter is shown in Figures 5.5 and 5.6.
For both scenarios, one has a flat plateau for low values of
the time constant and a sharp rise with increasing calues of
the time constant, i.e., for scenario 1, Figure 5.5 shows
the curves of the mean miss distances slightly increase up
to the time constant of 0.9 second, for scenario 2, the
curves up to 0.5 second are plateau, then for both scenarios
the curves rise sharply. For scenario 1, the mean miss
distance of case 2 is less than that of case 1 up to 0.5
second time lag, but above 0.5 second lag, the results show
an opposite trend. Also over this time lag, the curves of
case 3 for both scenarios are not sensitive to the variation
of parameter up to the time constant of 0.7 second for
scenario 1, 0.9 second for scenario 2. The general trend of
the curves, i.e., as the time constant becomes longer, the
miss distance becomes larger, is due to slow system response
From the figures showing the results, the time lag of less
than 0.5 second is necessary to obtain the mean miss
distance of the required order of magnitude.
Figures 5.7 and 5.8 show the variations of the mean miss
distances as a function of one sigma angle noise. For
1
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ItiiL LritioL LNbribLriLlNi Ul
5i
a-
a -t
to
0)°J
CO
._> a -
a d^CO
CO
._>
2Z C -I
cD-
od-
oo
x=*0.05 ( 1/secl CT=3(m) crb
=6(m/sec]
a = j = »15(dea/ a -^Cta's)3 w ,11 CX w
<7.=
c-, = .5 (dec/sec J PveY= 5 . 28 (rad/ssi
.nqaqement 1 Lme=b Isec
J
Le Velocity - 1000(m/sec
LEGEND
A - Z-^ 5;^:'-:::.: VECTORS
a- 4-MEflSUREMENT vectors
* 6_?1EF!SI!REMENT /ECTORS
~i 1 r-
0.0 C.2 0.4 0.5 0.8
Time Constcnt (sec
1.0 1.2
C T nRE] 5-5. MISSILE TIME CONSTANT VS
MEAN MISS DISTANCE
128
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HEflD-ON ENGAGEMENT CRSE
CDo
'
in
3
s^_, —
fOa>
or-
c-J
.->
o.3
03 CD0)
.-) CM
CD
a
x-O .05 ( 1/sec 1 crR=3(m) or^=6(m/sec]
a -a =.15(dea) a =20(q'3
LEGEND
A - 2-MEflSUREMENT VECTORS
D - 4-MEflSUREflENT VECTORS
O - o-MERSUREMENT VECT: : 5
i
'
0.2—I—i—i—:—i—|—r—i—i—:—;—i—i—i
—
;—|—
r
0.4 0.6 C.3 1.0
Time Constcnt (sec)
0.0 1.2 1.4
FIGURE 5-5. MISSILE TIME C0NSTRNT V5
MEAN MISS DISTANCE
129
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TRIL-CHRSE ENGAGEMENT CR3E
o
o•
ic -
co
.-Ja
"3I
CO
03
oCD
t r
0.0
x=u . 05 ( 1/sec j o-o=3 1 m ) cr£~6(m/sec]
-c-0.5(sec) a =20(a's)
er.-<x.-.5( deq/sec ) P M^.=5 .23!rac/sec)
Eaaaemsn.L TLme = Sisecl
Velocity - 1000(m/sec)
LEGEND
A = 2-MEflSUREMENI VECTORS= 4-flEHSUREMENT VECTORS
O = B-MERSUREMENT VECTORS
-j—r—i1 1
1 r
0.2 0.3
"'—1
—
r
0.4—I—1—1—1—1—1—
1
0.5 0.60.1
,rzLe Lrror la =a— 9 3)
de<
FIGURE 5-7 ONE SIGMR RNGLE ERROR VS
MEAN MISS DISTANCE
130
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HERD-ON engagement case
o03
£ O
03
CI
oCO
co ?-.J
= 0„05[ I/sec J crR=3(m] a6=5-m/sec]
^=Q.5i sec
)
a =2maxnu f n 'ol
4 Vj O ^
.5 ( daq/sec
)
DMfiX
=° ' 28 ( rac
Eqaaerr ent i l m 9 = q̂ ( sec
)
1 iLSSlo e Ve LccLty = 1000 (m/ sec
L. Li CL.I U
A - 2-MER5UREMENT VECTORS
a = 4-mersurement vectors
- 6-ilERSUREMENT /ECT0R5
—1—
'
0.50.0 0.1 0.2
RngLe0.3
Error (
a
0.4
(deg
0.5
FIGURE 5-8. ONE SIGMA ANGLE ERROR VS
MEAN MISS DISTANCE
131
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scenario 1, as shown in Figure 5.", the m . am mi
tance is obtai for the six measurement cas<
miss distance being . Lest for the two me ;
case. Above about the angle error of 0.5 degree the mi:
distance is not a strong function of the an L< error. For
small error values, i.e., less than (K2 degree, the curves
seem to be approaching each other. the scenario 2, the
miss distance is a strong function of the angle error. For
the two scenarios (Figure 5.7 and 5.8), the two measurement
case lias similar results up to an error of 0.4 degree. All
cases show a strong rise in the miss distance with increas-
ing error. The four measurement case seems pasticularly
sensitive
.
The variation of the miss distances as a function of
missile velocity are shown in Figures 5.9 and 5.10. During
simulation the initial stand off distance was increased to
keep the engagement time the same. One important aspect of
Figure 5.9 is that represents an opposite trend of the
vatiations in the mean miss distance to the general trend
of the variation in the mean miss distance. Under the
missile velocity of 700m/s, the mean miss distance is less
than 10m for case 3, from 15m to 12.5m for case 2, from 15.7m
to 14.5m for case 1. However, the curves for scenario 2
show the usual trend, i.e., the mean miss distance of case 1
is the smallest and that of case 5 is the largest among the
mean miss distances of all three cases at a value of the
132
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73 Ti -pupep FMQfiQF ,ri
CD-CD
X=0.05( 1/secl CT =3i^] oo=5(m/sec'R
t=u . b I sec J
; a _=.15(deq -20(q's)
a- = a. = .bideq/8 -J
sec J
-fix=6.28(rad/sec)
Lnaaqsmeni Ui"ne = ~ ( sec ^
:.
o
§°-.J CD-
00M
CO
CO
._)
21 aL
a. !
co-
co
LEGEND
a = 2-mersurement vectors
a = 4- me-e ,j^::/e
,
.t /ect jr
O = 6-MERSUREMENT VECTORS
400.0 6G0.0 800.0 1000.0
MlssLle Velocity (m/sec)
—
i
1200.0
FIGURE 5-9. MISSILE VELOCITY VS
MEAN MISS DISTANCE
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-e c:D-on en : : ,
2::
x»0.05(l/sec
CD
gs'Hco
CD '
CO
._)QCOM
|
CO 1
.-J
o-i
d-i
oCD.
o-R=Jlm
R
=201 .:'-
Enccqement TLme=5(sec)
/.=«ec 0 . 5 ( se
o- =cr^= .5 ( deg/sec ) PMRY=6.28(
cr.=
a-.= .15(dsq] a
dud'
EGENO
A - 2-MERSUREMENT VECTORS
- 4-MERSUREilENT SECTORS
O - 6-MERSUREMENT V
,A-
A"•A- -A-
-A
1—i—>
—
—i—i—'—i—'—i—i—i
—
r"—i—'—r—'
—
4G0.0 6C0.0 3C0.G 1C00.0
Miss Lie Velocliu (m/secJ
1200.
o
FIGURE 5-10. MISSILE VELOCITY VS
MEAN HISS DISTANCE
154
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given parameter. For both scenarios, the general trend
the curves slowly increases as the missile velocit;
increases, except for one curve of case 1. It la-
rized the foregoing, considerations as follows: As the
values of the missile and target geometry components become
smaller, i.e., relatively less effect of neglecting the
higher order terms in the first order filter and less
contamination of the measured vectors to noises, the mean
miss distance can be enhanced with the estimator of the six-
measurement vectors. As the estimator algorithm becomes
simple, the trend of the mean miss distances is less sensi-
tive to the target geometry.
Figures 5.11 and 5.12 represent the effect on the mean
miss distance of the total engagement time. For simulation
the initial stand off distance was increased to keep the
constant missile velocity. The general behavior of the miss
distances of all three cases slowly decrease as the engage-
ment time increases. For case 1, the mean miss distance
slowly decreases or is almost constant at below 10m. In the
other two cases, there is more variation in the miss distance
but the general trend is still downward with increasing time
to go. Note that as time to go increases for a given T,
one has more characteristic time interval available reflected
in T /t . The mean miss distance therefore should be lessgo
as the total engagement time increases. This fact is veri-
fied from both figures
.
155
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TfllL-CHRSF] ENG " ZNT Cfi:
a
'J2
O -tm
o
s=.
COM
.J
CO
co
o
ad-
a -
a
x=0.05 t 1/sec J cR= 3(rn ] o-^=6(m/sec) t=0. 5 (sec)
<j ( deq/sec
)
3 ~ ^'
a.=a.= . 15 ( deq) a =2Q(q's)e p ^j max ^
Missile Velocltu = 1000(m/sec]
LEGEND
A = 2-MERSURENENT /EC7QRS
D- 4-MERSUREMENT VECTORS
= B-MERSUREMENT VEC
-i r
4.0 7.0 10.0
Engagement Time (sec)
FIGURE 5-11. :ngrgement time vs
1EAN MISS DISTANCE
136
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h: cu-QN ENGAGEMENT CnSE!
.=0.051 1/sec) aR=3(rn] a^=5(m/sec] *-0.5(sec)
a "o- = „5(dea/sec) PMR =6.28 rad/sec]9 w
15(deq) on i >
-<c.U I Q s
Missile Velocity = 1 000 Im/sec
J
£ aa •
LEGEND
a = 2-mehsurement /ECTORS
= 1-MERSUREMENT VECTORS
O = 6-MER5UREMEMT VECTORS
oc
._;oa)°.
a-
oa.
4.0 6.0 3.G
Enqaaament Time (sec)
10.0 12.0
I CURE 5-12, ENGAGEMENT TIME V5
MEAN TOS DISTANCE
13
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Figures 5.13 through 5.18 >], the variation
in miss distances of ill three cases for side-
engagement (scenario 3) as a function : each para r.
The large miss distances resulting '
- m this, r:
d 3
motivated its separate consideration, General trends of
curves are similar to the other scenarios but with higher
miss distance. This scenario is a strong test of the
system. In this scenario, the control laws are subject to
the error due to the limitation of the small angle assump-
tion and the higher maximum acceleration capability is
required due to the large variation of the PZC-miss in
Y- and Z-direction. Thus there are some unexpected trends.
Figure 5.15 shows the variation of the mean miss
distances for each case as the a increases. Althoughmax &
the missile velocity is less than that of scenarios 1 and 2,
the maximum allowable acceleration is increased. As shown
in figure, the mean miss distance for case 1 is 11m,
for case 2 is 10m, for case 5 is 15.5m only at a = 23g's- max
The figure shows the miss distance variations in this
scenario are more sensitive to the a than in the othermax
two scenarios. The mean miss distance of below 40m can be
obtained at a = over 19g's, i.e., more a ismax & max
required than in scenarios 1 and 2. This trend of the mean
miss distances can be explained with the larger variation of
138
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the state vector in Y-di r .tion ivhi :] .-;'
Le In control :o land on .
As shown in ; Lgure 5.14, the variation?
distances of case 1 and 2 as a function of paconstant above P =4 rad/sec. That of case 3, ho rev,max ' '
continuously decreases up to P =6 rad/sec, thee, imax
almost constant. For scenario 3 one needs a i urn re 1 .
rate of about 5 or 6 rad/sec to have an effective BTT
missile, which is higher than in scenarios 1 and 2.
Figure 5.15 shows the variations of the mean miss dist
as a function of time constant. Up to the time constant of
0.7 second, the mean miss distance is less 20m. For all
three cases, comparing with the results for scenarios 1 and
2, the figure shows the curves of the mean miss distances
are less sensitive in this scenario. The target accelera-
tion vector lies in same direction as the missile velocity
as shown in Figure 5.4. Because the components of the
relative velocity are major components in computing the
control commands, the more correct control commands mav be
computed due to the small uncertainty in M,p
and Mbz
components. This results in smooth variation of the mean
miss distance curve. The trend of the miss distances as
the one sigma angle error increases, shows some different
characteristics from the trend for scenarios 1 and 2 in
Figure 5.16. The mean miss distances for all cases
increase continuously as the noise input increases up to th<
139
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OC3
W._)
OC*P
._>to
oO-l
aa.
15.0
S;CE-PPPPC C CP ENGAGEMENT CRSE
x-0.05(l/sec) crR-3(m) ^-6 -/sec)
a -a =.15(deq] t=0.5i sec]
o-, = 7.= . b ( deg/ssc J P M c>,= S . 28 ( rca'/ssc
Engagement Tlme~b(sec)
MLssLle Velccltu = 600(m/s8c)
LESEND
A = 2-MEflSUREMENT :::3R5
a - 4-mersurement vect .
O - 6-MERSUREMENT VECTORS
20.0
: lasmax w
25.0
FIGURE 5-13. MISSILE MAXIMUM ACCELERATION VS
MEAN MISS DISTANCE
140
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CD-CO
CD-I
CD
CJ
-J CD-
CO
to o 1
•-> CD5~ CO
J
aCD
O.Q
SIDE-flPPROnCH ENGAGEMENT CRSE
x=Q.G5il/SEC) <jR=3(m) dA=5(m/sec
a =c- =
. 15 (deal ^=0.5(sec]9 v-'
^=.5 (cleg/sec) aMflX
=23
Inqaaement TLme^olsecl
ssLLe VeLccLtL; = 600(m/sec)
LEGEND
A - 2-nERSUREM£NT VECTORS
D = 4-MEflSUREilENT VECTORS
O - 6-tlEflSUREMENT VECTORS
2.0 4.0 6.0 8.0 10.0
P (rad/sec)max
FIGURE 5-14. MISSILE MAXIMUM ROLL RATE VS
MEAN MISS DISTANCE
141
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SIDE-APPROACH ENGAGEMENT CASE
x=0 .Ob i i/sec ] CTD=3(m] o-a=6 ( m/sec ]
ao'
o!*>
CD
l_
->CO
._)
o3
to "-
0') n._> fM
ar.
a = a =. 15 ( deq
9 ^
cr.=
cr .= .5 I ^9g/s9!9 rD »J
max 3
=6.28(rad/sec)
Enaacement T :
^rne="-D ( sac )
Missile Velocity = 600(m/sec]
LEG L'
a= 2-!iEnsu^£ v£: 1 : VECTORS
D- 4-HEaSUREMENT VECTORS
O = 6-MERSUREMENT VECTORS
")—i—i—i—i—|—i—i—i—i—|—i—i—i—i—1—i—i—i—i—|—i—i—i—i—|—i—i—i—:—]—i—i—i—!—|—r~
0.0 0.2 0.4 0.6 o'.S 1.0 1.2 1.4
Time Constant (sec)
FIGURE 5-15. MISSILE TIME CONSTANT VS
EAN MISS DISTANCE
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SIDE-APPROACH ENGAGEMENT Cf E
aCD
'
o33
CO
CD
c
*->
03
._>
CDa
-
w Si
CD-CM
x=G.05( 1/sec) CTR="3(m] c^5 : :n/s8c!
or.-o-," = 5 (dea/sec 1 PMqY= 6 . 28 ( rad/sec )
t=0.5( sec
)
a ~23(q'<flax _/
Eqaqemerrt Time = 5 i sec
)
Missile Velccllu = 600(m/sec)
LEGEND
a- 2-riEfi5UREtfENT vec
D = 4-MERSUREMENT VECTORS
o- 5-:-'::;:.-i:"::.: vectors
t 11
1
1 r
a. a o.i
-i—|—
i
0.50.2 0.3
Angle Error I o- =c ) (deql
a . 4 0.5
9
FIGURE 5-16= ONE SIGMA ANGLE ERROR VS
MEAN MISS DISTANCE
14
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side-approach engagement cpse
o
— d jonCD J
IIa
a
od
0.05(l/sec] aR=3(m) a
ft-6(m/ssc] -t=0.5isec
3 5)
sg/sec j P..,cX= 5 .. 22 ( rad/sec )
1 ^ f H eg i =23 i q's )max -3
Engagement TL-ne = b l sec 1
LEGEND
A- 2-MERSUREMENT VICTORS
- 4-MERSUREMENT /ECTORS
- 6-MERSUREMENT VECTORS
4C0.0
"i r-
600. 80G.0
MLssLLe Velocity (rn/sec
10C0.0
FIGURE 5-17, MISSILE. VELOCITY VS
MEAN MISS DISTANCE
144
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SIDE-RPPROflCH ENGAGEMENT GPSE
X-0. 05(1 /sec] crR=3(m] a^6(m/sec) -=0.5 (seel
, ,—r!D
U)
u
D-Jw
._)Qa
CO r-i
W a._> *r
6.0 8.0
Engagement Time (sec;
10.0
FIGURE 5-18 ENGAGEMENT TIME VS
MEAN MISS DISTANCE
145
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o-
C3-1to
oto
CD o
ca
CO
CO fCO
OHCM
O-t
x=0 .05 ( 1/ssc J crR=3(m] cr^=6(m/sec J t-0.5(s8c"]
a "a -. 5( dea/sec ) P Vq Y "5 .23 ( rad/sec j
3 w I In A
a- = CT-= . 15 ( dea ) EnqGqement Tlrne =5 ( sec )
LEGEND
A - T^iL-CHnSE: ENGAGEMENT
C - HERD-QN ENGAGEMENT
D- SIDE- RPPRORCH ENRGHGEMENT
0.3 Q.a
RngLe Rats Error (dsg/sec
FIGURE 5-19. ONE SIGMR ANGLE RRTE ERROR VS
MEAN MISS DISTANCE
146
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one sigma error of about 0.4 degree. \bove the one sigma
error of ''>. \ 8 degree , case 1 d then rises :
case 2 continues decreasing slowly, case 5 increase
continuously. The variations of the mean miss distances in
scenario 1 are almost plateau and in scenario 2 are almo I
constant up to the one sigma angle error of about 0.4 degree
then rises sharply as shown in Figures 5.7 and 5.S. rhis
reason can be deduced from the fact that as the one sigma
angle error becomes large, the same magnitude of the two
state vectors in different direction, i.e., relative
distance in X-direction and relative in Y-direction, having
the same order of magnitude ma}' be contaminated to the noise,
on the other hand, the other scenarios, only the components
in the X-direction being relatively large are contaminated
to the noise. From this fact, it can be concluded that the
missile in a side -engagement case is more sensitive to the
angle error than the other scenario.
Figure 5.17 shows the variations of the mean miss
distances as the missile velocity increases as a parameter
(i.e., holding the total engagement time of 5 seconds and
changing the relative position of the target). Up to the
missile velocity of 700m/s the curves of the mean miss
distances are plateau, after that velocity the curves rise
sharply. Figure 5.S shows the variations of the mean miss
147
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distances as a function of the total engagement time I.e.,
holding the constant velocities of the missile
target ana changing the relative position of the target;
scenario) .
figure 5. IS shows the mean miss distances rise sharply
over the engagement time of 6 seconds. This is somewhat
unexpected and may be an artifact of the implementation
procedure. In other scenarios the mean miss distance as a
function of the missile velocity increases slightly as the
missile velocity increases and that as a function of the
total engagement time decreases slowly with increased
engagement time. from above analysis one has a conclusion
that the computed control commands should converge in the
final time as the computed commands of the two- four-
measurement case shown in Figure 3.32 and Figure 5.54 to
obtain the low mean miss distance. However, it can be
expected in this scenario that the computed control commands
do not converge possibly due to the violation of the small
angle assumption in deriving the equations of the control
commands (I.e., in this scenario the M. component is
large in Equation (5.7), that causes the large mean miss
distances). However, in the case of relatively small magni-
tude of the missile and target geometry components, the
error caused by the violation of small angle assumption is
relatively small comparing with the errors caused by the/ it o >
contaminated of the measured vectors to noises,
14 8
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limitations of the control inputs, an I he estimation
tSic state vectors using first -order filter. Ph ;
verified with the results chat the mean miss d:
11m for case 1, 10m for case 2, 14m for case 5 at the
missile velocity of 600m/s and the engagement time of 5
seconds, as shown in Figures 5.17 and 5.18.
The final Figure 5.19 shows the variations of the .
miss distances for case 5 of all three scenarios as a func-
tion of the one sigma angle rate error. Ihe result repre-
sents that the variations of the mean miss distances due to
increased input values of one sigma angle rate error are
almost constant for scenarios 1 and 2. For scenario 5, the
mean miss distance varies at constant rate of inclination.
This slope of the curve is relatively small comparing with
the slopes of the mean miss distance curves as the other
parameters change. From this analysis, it may be concluded
that the exposure of the measured vectors to the angle rate
error noise is neg legible in the case of present study.
149
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VI . CONCLUSIONS
A simple but: effective biased guidance lav; and t ree
extended Kalman filter equations have been deri ed from :
optimal control theory for a bank- to- turn missile with zero-
lag and with 0.5 second time lag autopilots for pitch accel-
eration and roll state.
The equations of motion are linearized around the present
orientation of the missile with the assumption of small
future roll angles. During computer simulation, the optimal
control laws and estimators were tested separately invoking
the separation theorem. In the simulation of the system
three optimal estimators were used, i.e., estimator
with two measure:.! cut vectors, with four-measurement vectors
and with six-measurement vectors. The system has been eval-
uated for three scenarios, tail chase engagement, head on
engagement and side approach engagement.
From the analyses of the simulation results in Chapters
3, 4 and 5, it can be concluded as follows: The control law
was successfully simulated for a hypothetical bank-to-turn
missile with pitch acceleration and roll rate autopilot
within the imposed limits on three scenarios. Miss distances
with zero- lag were negligible for all three scenarios
(below 0.5m) . For the time lag case one had miss distance
of 5.9m for scenario 1, 0.8m for scenario 2 and 32.9m, with
150
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0.5 second lag and with arnnx= 20g's. By Li irposi ig a
higher maximum g of 2 3 on the scenario 3, the miss
distance was reduced to below 5m miss distance.
expect a high g requirement for a side engagement situa
tion as in the case of scenario 5. It is evident that the
missile's tracking ability against the target is very
sensitive to the missile and target geometry, the missile's
manuevering capability is also a very important factor.
Optimal control laws applied to the side approach engage-
ment place a severe strain on the system from the viewpoint
of the small angle approximation. Increasing the number of
measurements did not result in increased performance in the
sense of smaller miss distances for the missile except in
isolated cases. This may be due to the increased com-
plexity of the system as the number of measurements was
augmented. Further, in this stud}' only first order extended
filters were implemented. It is possible that better
performance could be obtained at the expense of increased
computational load, by using a second order extended Kalman
filter.
151
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APPENDIX A: PROGRAM LISTING
This appendix provides listing of the computer prog]
used in the present study. Only one program for the six
measurement case is provided. Except for a few differences
in the subroutine filter the programs for the A./o other
cases are identical to the program included here.
152
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c//SK// £
//FO
Cc ** »
IN BCSECr i . a
IE
*fS^ T
DIDI
CICILIDIDILILIEIDICCCCCO
J03 f
c HTX CLiSIN DELICITAL*4 IS.E1 ,£52,G§2KENSICKENS 10kl:;s :o* ENS TOI £ KS I
C
KENSIOSENS 10HSN5I0KENSIOE ENS 10KENSIO
KENSIOR110N /CBHON/TflMON/I
CCCJ: I - E r
SHIN BOH XCJi ' ,CLASS=E
5 E A L *
,AAC
1-TAG1 1
CI3 6El ,2ho (2PI (2?6 | 2
XMIS
/
6 \
S)
CO
|2f
;c/iSE;
(• - r
I I ti .
) ,«.<
' 'c £
) ,H! ,E2
'•'f''
C,AI1.42
r— 2 '
C Ra7' - • «
S .§ )
r3ANK,IGl, TG2 :'
1 , 02 , U 3 , 3-i , 05 , Li- , 3 7 , U3l,£1 ,P2 ,E3,E4,?:3, fo,?7,Pc,P9, G 22
.
C (2 3
(20(200i.
I . I 3
(20 .j
I200xai s
r yj . SItu, A
2,A2
E8.E9,
LEI. |S3t ( 1 6 ).
, W W { fc
! ,P?C
1 0) r G3, u 3 (
:
, u a (
;
,2 3 u,E8 U,?3 (2,?a [2
! (50) ,
"iO, A
I
,A31 ,
,9) r Q{9,9) ,B (o,oj ,X (9) ,? (9,9) ,E I)
,X2 (9)
i; )) , £ Al K (203) , TGI (200) ,TG_ (2 2 0)
1 (200) ,G4 i (20 J) , G5 1 (203)CC, ,UH (2CC) , U5 (23J)CO ,U9 (200)CO) ,£; (2 )0) , E5 (230)CC) ,E9 (2CC)CO) ,?« (2J0) , P5 (200)CO) , ? 9 1 2 C
)
ZMISS ( 53) .DMI3S (50)
ZO,CTX , CI T, CI 2, 02, ISPIN.TACC}32,;>3 2 , 2 1, £2
»»iM«»****4**t*HM**S***?+***44*?*?*t*»*****
I N F U I D& 1
2
DII=.0HCASE=N£AX=1KEEINTLPP.IN2IIICT=IPIO=0ESIO=0PHI 0=0V£0=10X H C-= .
XEO=0.ZKC=0.XI0=25Y I C = .
ZI0=-1V 1 X C =
5
VTYO=0vazo=cE1=.00B2=1.2ATXO=0Al i C=uATZO=0C 1 X = ..
CT X = .
C1Z=.0S£l=5.
51
10= 1
= 51
30
00.
00.CC
325a
,-*S.8
55*S.(20.026C87026087
521 =
RZ2=.uiE23=.0(RZU=.GiHZ5=3.RZ6=6.SB1=.0<SH2=.0
'
SE3=.0!sai»=_OiSE5=3.SB6=6.SFCS=3.SVEL=6S«CC=9.1SPIN=.TACC=.
5
A«AX=2C.=»S.3
I 026iC3726
i 08 7
. 5
15.
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4HIN=- i. »5E K 1 X=6 - > £
Q F S C=0 .
ISEED=675cc ****** *-****cc ISITIALIZIc
j**;,S***.»«x« ********** < - -*jr J<^-
S , « , F I
DC 1:0 1= 1, )
DO ICO J=1,9DEI (I, J) = 0.I: |I.3Q.JJ DEI |I,.A(I,J)=DEL(I,J): (i.J) = o .
10 CCKTI2JUEVIC C0NTISU3
DC 130 1=1,2DC 12 J J=1,2a II, J) =0.
120 ccsrikuE13C CCSTINU3
B (1,1E ( 2 , 2R t3,3a (4,4S 15,5a ( c , o
= SE 1**2= S r 2 ** 2= 3 F 3** 2= 5 F - ;' * 2
= 2 ? 6 -1 * 2
a (1,4) =Dnp. i2,S ---d:tA (2,o) =DI1
CAIL FUNC ( SAT,CI, CI T f ?1 , F2 , ?3 , Q1 1 , Q 12 , C. 13 , Q22 , 3;23 , Q33)
II #2) =-°l(«,7) =F2(7,7) =F3
(2,3) =F1lb, 2) = F2|c,d) =F3
IE, 8) =s
=F1
= C 1/
IS, 3) =.,
154
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13a567
Q (5,6) =Q22C (9,9) = *33
KErrE(6,5).'RITE (6,6)dc u-. 1=1,9WRITS (6, 7) i (1,1) ,Q (1,2) ,5(1.3) ,Q (I, 4) ,C (1,5) ,3(1,6} ,Q (1,7
' ,"
! I J ) , C ( I , J )
cc:*:::; U2FCR3AT (MM
":5X, 'C61GISSI. 2 MATRIX'//)FC R M A I ( 9 ? 1 4 .
'6
)
fcekat (//-< x, ' ; KaiRix «iih ?s:u:; jjcish, J?3U=« ,75.3//)
ACL FSF.UIC 3AT2IX
F11= (JPSU**2) * (CT 1**4) /4.?i2=to?sL'**2)*(D'n**j)/2F22= (Cjpsu**2) * (.: ; i**2)
C(1,1)=C(J,1) *|11Q (2,2 =Q |z,2 +F11C (J, 3) =0 u,J) *r1 1
Q(c,b)=Q(6,6)+F22
Q (1,4) =Q (1,4) + F12C(2,d)=QU,d)+F1.<:2 (3,6) =Q (3,o) +F12
Q (4,1) =Q (4,1) +F12C (5,2) =(J (5,2] - .- 1 -
Q(€,3)=U(6,3)+F12:«RIT2(6,g) ,?HCC 135 1=1,9
" (I
>)
*RI1SJ6,7) 2 (1,1) ,C (1,2) , £ (I ,,,,
, C <I,."1) , £ II, 9](1,5) , 3(1, °) ,s2 (1,7]
135 CONTINUE
C C 15 1=1,9CC 1-J3 J = 1 , 9AT (I, J) =A (J, I)
140 CCXTINUEISC CCSTI2J02
5.31X2(6,5)
SIART CASH LOC£
DC 950 111=1 , KCASE
i.nitiai.::z
s=cK=KPRINT-1L=L? RI NT -1ISTCP=0DT=DIT
IFfN'CA SE.FQ. 1) GC 1C 16 )
K£Si:iT = lC jOO 3
LE8iNT=icccoa1 1 LOT—K = C1 =
16C CCMTISOE
Y(1) =0.Y (2) =THTCY j3 =psi
c
i |a)=vaoi (S) =xsoY (5) = YMO
155
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i (7) =2 210•£ (£] =V1XCi (S) =v :ycX (10) =vmiI 12)X (13)
? 115)X(16
= X : j
= ; l C
= P H 1
1
= C*.
DRAW 2 A :«' C C S
17 C
180
1902CC
CALLc;;lCALLCALLCALLCALL
l, >i c ? i
v c;nc , '.-. , : ;
j a x , l, i
i. NC £ a { LS c £ C , 1 2 , i MA X , 1 , 1
LN05S (ISE.E! , *3 ,1 MA £ , 1,1L.NC EH (ISrEC, *U, :.:'.AX,
'
SO E M (LSI;t :,c;
INITIALIZE ? Mi
CC 180 1-1,9C C 1 7 j J = 1 , 9E 1 1 , J ) = .
CCNTINLEc c :>:::;•;
:
E (1 , 1) =S ICS**2? (2,2) =SFCS* '2
E [J, J) =5 £CS**22 (4,4) =S V EL * * 2
P (5,5 =SVEL**2F |b ,6) =SVEL**2? (7,7) =SACC**2E (8 , 3 ) =S A C '
-
P (S,9) =SACC**2
DC 200 1=1,5lC 190 J=1,2GAIN (I, J) =0.CCMIXOECONTIN US
n , .* SA .'. , I , I
i :
x
70C
£ I A I! I IB
CONTIS (IE
S=fi + 1
K = K + 1
L=L+1
SCICE: LCCE
C5EINZ IXTEGSATICS VARIABLES
T=I(1)7 R I = Y ( 2
)
ESI=Y (J)v <
v. = y { .i
;
xs=fYM=3f (bj2*=Y (7)V1J( = Y | B
VTY=Y 9iVTZ=Y 10)XT=Y ( i 1)Y1 = Y (1 25ZT=Y (13)PHI=Y (14)£ fi- Y { T 5)A H = Y ( 1 6
)
INE3TIAL 10 BCDY 2X1 IHAKSFOSKATION
A11=DCO£ (1HT) *ECC£ (ESI)A12=CCC:,- (THT) * E S I :; (ESI)A13=-D£I.\ i ;h r)
A21 = D5i:i (EKI) *D£IB (THT) *DCOS (ESI) -DCCS (EHI)* DSIII (PS I)
156
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A22=DSIN (PHI) *C=I!i (IHT ' .' ( SSI ) -DC CS i I £ £ )* DC 3S
I
A23=33 Hi (EHI) • CCCS (I
a31=dcos i: L) :_:.-:.•:, *dcos (ESI) + 33 [j;<phi) * 331:1 (?si)
132 = DCC3 > *C£IS (TfcT) *CSIS (ESI)-DSIfc (£HI)*DC ,
A33=DCC3 |PHI) 4 CCCE (TEX)
vkx=ai 1*VK• 3*=A1 2* ,iMvez=ai J*va
cc CG2PUTE I?c
BX=XT-XMg Y = XT- v '
EZ=ZT-ZH
UE CCXECSESTS 01 7ECI0B
VEX=VTX-VKXVR^VT*- SI! £
V5Z=VT2-V£Z
aix=Axxo*EExs i-c~ft i 2 = A £ : 3*33 X? (-C : 1*1)4TZ=ATZ0*CEX2 (-CTZ*T)
X2 ( 1) = BXX2(2) =B'iX2 13) =32X 2 { 4 ) = V ?. ;(
X2 i 5) = VBYX2 (6) =vh :
X2 (7) = ATXX 2 ( 8 ) = A T X
X2 <9) = ATZ
1GC=-{5X*VHX+EX*VEY + EZ*VHZ) / (7SX**2+ Vra**2+'/aZ**2)
£C£;J. MEASD3H:iE5I VEC1C:
S 1 = 33 2ST (SX**2+SY** 2)S = D SQ3 1 ( £ 2 ** 2 -£':'- * 2 + SZ* * 2
)
3ET= (3 X*VEX+S i*VRY*SZ*VaZ)/STHTS=-DATAN2 (SZ,S1)P£IS=DA2AN2 ( 3 : .£>)T£TSD= ( (-CSIM (TKIS)*CCOS (PS IS) *SX*VB X) - (3 3 IN (THIS) *DSIN(PS
*RY)+ (DCOE (TH1SJ>S2*V5Z) ) / (S*S)ES13L= ( f-ESIN (Sill) *Si*VRX) + (LCC3 (PS IS) *ST*VaY)
) / (5*S* 333 3BEIS=3BDI£D= (SX*VHX+ £ i*SE Y+SZ* VSZ) /S
Z I 1) =IfiIS 03LE [il (N))*SZ1Z 1 2) - r HT £ E + C E L E ( « 2 (
v
.i )
- 3 3 2
Z ( 3 ) =? 31 5 +-D 3 1 E i . 2 ;'..))* S Z 3
13) * 3 ": • Y
(THIS) )
Z (4.2(S))*SZ5Z (5) = 2EIS fCsL: (
Z (6) =8DI£E+D3LE (h2 (il) ) *SZ6
IHITIAIIZE SIA1E T«£C10?.
IF (N-GI. 1) 30 TO 750
CALL LHCSK(ISEEE,Sn,-6,l 1 )
X |1)=RX+E ELE ( JiK ( 1) ) *SrOSX (2) =R X+ C ELS ('.'.>
( 2 ) ) * SPOSX (2)=aZ+LEL£ (Vifl (3 )
* S20S
75 i
7. |4)=V HX+3313 (Si -. (U)i ( 5 ) = 7 B X + C 3 L £ ("»
'* (5X (6) =VHZ+CDLE | vi • (6X (7) =0 .
X (8) = 5.X(S}=0.CCKIINUE
SVELv : i.
7 3L
LI. Li: L ,
-
C12 = DC0£ ilHlS) *0:C13=-DSIK [TH .. !
XIC3 = D 11*X(1)+C12*X(2)+D1 : X(3)
157
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GENERATE GuI^ANCE CCf.MASDS
CALL 3 DICE (X,IGC2 ,AC ,PC, CI)
IF (AC. GT.+AaAX) AC = A•
If (AC. LT. -AMIS) JC--.''.:!';
IF (PC GT.+t HA :<) PC=E KA !
I? |EC. LT . -::::.:;; [C=-EKA <
I? ITSPIN.IE- J. ) r:"=^CI r ( TA C C . L E . . ) A fi = A C
T0?.2 VABIA31 ? C a E LO -
P H ID=?
AAA [MlEE2 («)B A N K (
N
TG1 (>!)
1 G 2 ( :i
)
1 <N =
U2 (N) =
U3 (.,) =
U« (») =
:: 5 i.s )
=
U6(J! =07 |!li =Gt(.N) =U9 |N) =
El ()•') =
E2 (N) =E3<N) =E-4 ;:,} =
H5 (N) =• [K]
2 j (N) =
EeiM) =
2SJS) =E 1 1=05P22=?5E33=DSP<44 = QoE55=0SP6c=0SE77=05PS8=DSE99=D5PI (N) =
£ 2 |!l) =
? 3t!,;i
=
EU (N) =
to (N) =
FS |N> =PS (V) -G1 1
G21G3 1
G 4 i ( :<
)
g 5 i ( y
)
LIKE 3 If
HI*57.3
= AC= £ C
J"= ehid
=TGC=TGC2ax?.Y
7 3 Y
v a za: :
lis!
X (6)
lie7
!
x I s
)
Catsai i
;ai; -
:
2
(1
(K.
.:• I ( P (
6
C31 I? (7caifP(d
P1 1i -
P33P<4 a
F55?6c?7 7
F8 8?99=gai:j (1,i=GAIN (2,1= GAIN (3,
-
=GAIN [4 , 1
=GAIN (5,
'
LINE E;
T . XEE I
S
=*5
, 1) )
;r);
fT? MCASJ D
K = CTHTD=?HT*57.3?£ID = ? SI^57 .3PHID=PHI*57WHITE [5, 1C]WHITE (6 , .0)HHITE (6, 2C)
) GC TC 250
"i
3I,£,XLCS -IGG-TG02 .AC.AH. EC, EM- PiT, EX,SY,SZ, /BX, 7SS,7RZ, ATX,AIY,ATZI,X(1) ,J (2) ,1 (3) ,.1(4) ,X{5) ,X{6) ,X('
rzX(7) ,x{8) ,
-,: ;j)
158
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102025C
30C3C•4
3dC
U0!
50
iSi:: (6, 2C) r,E11,E22,?33,=>i |,E55,?6 6,£77,E88FC5MAT(/1CF13.3)F C £ a AT ( 1 f 1 3 . 2 )
CONTINUE
15 IL.LT .LESI NT)L^J
TC 3 50
BBIT2(6,3C) :
DC 300 1= 1,9KB I IE (6, i*C) 3AIK <I # 1) ,SAIS<I , 1) ,GAI'J U, 1) , GAI>i (I, 1) , GAIN (I, 1)
FORMAT (//S7X, 'GAI2>F C E
v:, : [52X,6F13.5)
CCJiTIIi US
93
K A IH (T =, £ 3 . . ) V)
CHECK FOB I S A 1 I C K
STATE VECTOR)
IF (SDT .GE.G. .CS .:gc -LZ . . 3) GC TC 900IF |IGO. GT.iT) GC IC UOOCT=TGOisicp= i
CCNTI?IUE
INTEGBAIE TRAJECTORY ONE 5TEr 8HEAD
CALL INTEG (X,£)
IF (ISTOP .GT.O) GC TC 9 10IF (N. SE. :. .MX1 CC IC 300
EXECUTE SALMAN Fill Ft (UPCATi
CSII FILTER (X,E,E,Z,GAIN)
GC 13 70C
IEESIHAL EBINT A .\ C E LOT
CCSTIttUEt=y{1)CX=Y (1 1) -2(5)DX=I (12 -Y 6CZ=i|1j| -Y (7)CM=DSQi?T |EX**2+CY**2+DZ** 2)XfISS (III)=DX
ZMISS (II1)=DZCMISS (iiiHdmSRIIE ( 6 , £ C) I , E X , C Y , Z Z , D M .1 IIFCfiMAT (// SX, ( I= ' ,F 10. J , 5 .< ,
' DX = ' ,F 10 .3. 5 X ,' D Y= ' ,71 J.3,5X
ic,
, DZ= , ,F1C.3,5X, , CISS = , ,F10.3,5X,'CASE=' / I7)
IF (IPLOT-NE. 1) GC TO 950
CALL P LO TG (TT , E 1 , N , 1 , 1 , 1 ,
*,11,0„.0.,0.,C.,5.,5.)CALL ?LOTG(ri,U1 / N,2,1,Q,
* ,11,3.,0.,().,C..3. I5.)CALL PLCTG (TT <Ez,S,1,1, 1,
*,11,O.,0.,0.,C.,3.,5.)CALL PLCTG (TT . Uz. S , 2 , 1, ,
* , 1 1 , . , . , J . , J . , d . , 5 .
)
CALL PLCTG iTT. E3 ,N ,1 ,1,1 ,
*. 11,.;. ,0..3.,fi.,3. r 5.)CALL PLOTG (TT . C3 . N ,2 , 1 , ,
* , 11 , . ,0.,G.,0.,5.,5.)CALL PLOTG (TI , E4 ,H , 1 , 1, 1 ,
*, 16,0. , C-,0. ,C.,5. ,5.)CALL PLCTG (TT ,0U,S, 2, 1, 0,
»,16,0. r 0- r J-,C.,D.,5.)CALL PLOTG(ri .E5 1 >, 1,1, 1 ,
*,16,0-,G..O.,C..D.,5.1 „CALL PLCTG (TT . C5 , .\ , 2 ,1 , 3 ,
* , 1 b , . ,0.,0.,0.,o.,5.)CALL PL01G(TT,E6 ,N',1 ,1, 1 ,
TIME |SEC)
TIBE (SEC)
lit I [2F.C)
TIME (SZC)
II HI (SZC)
TIKE (SEC)
TIME (SZC)
TIME (SZC)
TIME (SEC)
TIME (SZC)
TIME (SZC)
',10,'
',10,'
1, 1 C ,
'
', 10,'
',10,'
',10,'
',10,'
',10,'
',10,'
',13,'
',10,'
RX (METERS)
SX (METERS)
Tl (METERS)
RY (METERS)
?.Z (METERS)
RZ (METERS)
VRX (METSHS/SEC) '
VRX (M3TEHS/SEC) '
VRY C-iZTZRS/SEC) '
VRY (MSTS2S/SEC) '
VRZ (METERS/SEC) '
159
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*,16,0.,C.,0.,C.,5.,5.)CALI ?LOIG (?T . 06 t S, I , 1, 0,
call ? L a r g i t r . :• 1
.
:. , i , i , i
,
*, 13,0- r 0.,J.,C.,3.,5.)C.iU ?L01G(TT,C7,N', 2,1,0,
*,19,0.,0.,0.,0.,5.,5-)CALL ?LOTG (Tl . •
fK,1 ,1, 1 ,
* , 1 9 , . , . , . , fl . , 5 . , 5 .
)
CALL PLJIG (II , Cc ,:. , - , I, J ,
*,1S,0.,0.,0.,i.,,5.,5.)CALL ? L ,: 1 G (rT,£9 z N, 1,1,1,
*,1S,iJ.,3-,U-,C-<D., .
C A Li. -: _c: l- i'i .'.',:,.,!,. ,
*,19,0. ,0.,0.,C.,i.,5.)CALL ?LC lG CI, 2 , «,1 ,1,1 ,
*,0.,0. ,0.,0. ,5..=.)CALL PL^IG (T'I,E2 ,N,1 ,1, 1 ,
*,C.,0.,0.,u-,5.-'d.)CALL ?LCTG(TT , Ei,!.', 1,1, 1,
*,C-,0.f0.,0. ,5. ,5.)
c a L ls
? l c i g ( i
:
,: y , .. , i , i , l
,
*,C.,0. ,0.,0. ,:.,5.)call ?l^; 3(T r
r .t5 £::,i ,1, 1
,
*,0.,0.,0.,U.,5.,b.)call plotg (ii ,E6,5,1 ,1,1 ,
*,0.,0.,0.,0.,5.,S.)CALL PI,G3G(T'I,E7,S,1,1,1,
*f 0. f 0.,0.,0. f S..£.)CALL PLO TG (T T , f 6 - S , 1 , 1 , 1 ,
* , Yj . , ^ • , O . , o . , _ . f ^ »i
call ; l : : g , r : . : r , ;. , i , i , i
,
CALL ? LC T G (T T , A ; C <i , 1 , 1 , 1
* ,' Mosa m a_c_l:: >1ic (.1/
CALL PLC! G (T T , 1 I. li , 11 , 2 , 1 ,
* ,' NOaa . L ACCELEi ftllCJi (."!/
call ?lc:g ,n ,eec ,:; ,•
. , , !
*, BCLL 52TE I BAL/SEC) ', 19CALL PLOTG (TT , E23 ,N ,2,1 ,0
*, 'EXLL BAIL [fiAE/S£C)',19CALL ?LCIG (TT , E ? .. .- .N ,1, 1,
*, 'LANK A SGLE |CEG) ', 16,0.
CALL ?lcig(::,ig:,::, - ,i,i*, 'TIME IC GO I SEC) ' ,1b, 5.CALL ?L01G(TT ,TG1 ,N,2,1 .0
*
.
'TIMS TC GO (SEC) ' , 16, 0.CALL ?LC1G(TI,G1 1 ,N,1.1 / 1
*, 'G (2, 2) ' ,6,0. ,0.,0. ,0. , 5CALL ?L01G (II ,G2 1 ,li , 1.1 , 1
* ,' G < 5
,
2 ) ',6,G.,C.,0.,0.,5CALL PLOT G (TT,G3 1,ii, 1,1, 1
*, 'G (8,2 ' ,b,0. ,C. ,C. .0. ,5.,D.)CALL ?LOTG(TT,G1 1 ,N , 1,1, 1, 'TIKE
* , ' G (3 , 1) ' ,5 , C . , C . ,0. ,0- , 5 . ,5 . )
ciai iszz)
TIME |S EC)
PI 81 (SEC)
TI3E (SEC)
TIKE (SEC)
i : ;•:•
naiis •
i i
riKE (SEC)
TIKE (SEC)
TI3E (SEC)
TIME (SEC)
TIKE
ri:
(SEC)
(SEC)
IS EC)
(SEC)
(slc;
I '
i
s c * *
:
(SEC',30
XML I '-J
I J. .. I U-vEC^*,) • ,30TIM
. , C . , J . ,
"TI2E (SEC0. ,C.,0.,0, ' i
i
:' ei
J . , C . , . , 5 .
'TIKE (SEC. , . , . , 5
' PIKE (GLC) . , c . , d . , 5
1 ™ - '« - / I 7 £
l TI?.E (SEC,5.)rial (sec
CALL PLCTG(TT,G5 1 ,"J, 1,1 , 1 ,' TIME**.\9S6 l}11'<?.0. ,C..Q. ,0. ,S.,5.)CALL PLOT (0. ,U . ,?99)
(SEC
1 C , ' 73 z ; )'
10, ' \:/.v
' ' :-
10,' ATX ( iETEHS/SEC **2)
'
TO, ' ATY {::£IL=S/JEC~ J'.0 '
10, ' '."•
(! ET2aS/SEC**2) '
10, ' ATZ ,'.:: 3S/ sec i ^2) •
. -.i: (as i!Eas/ss ;**2)
10 , 'SU2T (P11)
IC, »SGHT(P22)
10, 'SQRT(?33) ' ,9
ic , • sqst (?uu) «
,
10, SQST (?55) ' ,
' C
' S QST ( P7 7 ) »
,
'SCjHI(?S8) '
•SQSI (i?9y) •
10,
10,
1C,
IC,
,10
; i
:
/-
ir3.), 10
, 10
, 10
, 10
, 10
. , . , . , 5 .
* f J • t \j » j b
5.)
,5. !
95 C CCHTINCE
i CCaPQTE KISS DISIANCE STATISTICS
17 (HCASS.EQ. 1) GC TC 975CALL STAT (XaiSS , :!C AS E ,XB AS,XR£S)CALL SIA1 iiJlLL;,'.GA5:: ,:-;^, fSCS)CALL STAT (2.V;ISS, '..CAS E,Z3A5, 3KS)CaLLSIAl(Dai£S,NCASE,DBAo,DSl!S)S SITE (b, ij_
H£ITE(6j6C) XE8E,XEKS«HITE(6,"/C) YE A 5, 'iR S3«EITE(6»60) ZEJ»B,ZEKSWRI1E(6,SC1 DEAE.E5KS
55 FCE-MAT (//13X, • KISS CISTA^iCL STATISTICS'/)6C FCEMAT (/Si,' X-CCMECSENT' ,5X, ' XEAN=' , SB. 2,5X,
'
aHS=',F8.2)
160
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7C Foaa.s r f/sx, 1 y-cc^ic:;30 .- ' • : !/ 5X, ' :-^:c::^-:. IT
FCEaAX (/5.(, ' :c:.;: . :i .
'97 5 CCSTINUESIC?
, 3X, »KEAI, . ,
' ȣANa 2 , 5 .<
,
?.as = '
161
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50
103
SCBHOOTISfi jCIDE (X ,TGO, AC, =C,CT)I2EIICIT 6EAL j
E [1-r ,
DIMENSION (9)CCEaOK/TfiSEE/ 2;i,a2;,A23,A31,a32,A3 3,31,3;
= 9.3
MZ = X (S)TGC=- ( FX*VHX + FY 'FY + EZIF (TGO.Li.O.) GC IC
) / ( VEX >*2 + VSY* '2 t-V3Z**2)
F0= (CT*TGC-T. +E2XE (-CT*TGC) )/ |(
ZEKX= 3 X+ V EX*~GC+ •
ZEKY=P. K+ liEX*TGC + AIY*FQZEJiZ=RZ+VEZ*TGC + ATZ* EO-. 5 '1* (TGC
Z EZ = A3 1 *ZE ax + A 3 2*Z£*Y + A 3 3 *Z E3 ZP 1 = 3.* TGC/ (3.*E1 *-IGO**3)ac=-?i *5
DEHI=DA?AS2 (ZEY, -ZF.Z)F 2 = 7 . * AC * AC *TGC* ( 1GO **3 * 3 .* S 1 )
F3= (AC*AC*TGO**5) (c.3.*32)PC=F2*DPEI/F3GC TO IOCCONTINUEAC=0.p C = .
CCS1INUE
£5C
162
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55
60
65
7C
75
3UE3C3IHELIC
COMMONG = : .
:iur -,: =
THTC=0FSI0=CVC=DEI.: 2=t ...
•£C = u^I
7 XC = V0r::= : j
7ZC=-V
riSS E?iT2G (EZIK,a:IT 52 A L * € (i! - H , 0- Z
)
lOi DJSIM ( 16) , EEOUT { 16) , Zl 1 ( 1 )
,• r»C/ SC,EC,ATXO, EKO ,aizo ,ctx
16
MS
, cx
;
r OT , .
* C £1 N ( I H 1 )
(?S 10)
NUHE2ICAI IN TEGS A TIC N ( •08 ESI RUi ; j - SOI
DCT=D1HIESIv = ::
PHIA .
v.=
EH=I? (
1* I
DSCDECCZCDECDECegoZ iCD ECDECDECEECDEC- v p
;
dec:DEC!eec<i? (
IE (
DCIF I
if!DE1DE2
GC"USECE2USEGCUSECZ2USEGCUS?USEDEI
75 .11=1,4SIN (1)
= p£,IN I :
Mil)
IS?I A CUT (
UT(UT(UT (
UT
C. G
a:i) =
5!
(7)UT t 6
]T{9)DT(10)UT (11
1213141516INC.
la i .
I. J
1
.
: (A.I
'Ail*-Q*
' '. * D
V * D
A T X& r :
= AT
!f'»!
)=1
!=C SIN (1 5)=C E I S ( it)]
5 ItHI) -G*CCO£ ITHT)) /V!r: : ij /{v*ccos r... :
;
IIHT)1KT) *~C23 (?SI)IHT) *DSIN (PS
..:i
-ctx*?)XE <-CTY*T )
£XP(-CTZ*S)
75ai.* 1
.
21.
i=6TO1 = 3
HiTO1 = D
Eg.- ^
EC.= D;= D.E275EG= D5 E
75
rQ3= e«= 0.= J
.
GI.T. J
-;;G
2)4)i:<'(
CUT
(1.
; _ :_ i cr
: i n 5 ;- ( ? c - 1 .- ) /ts e i k
To =8 A IGC TC 60GC TC 65GC TC 7
I) »CT
c i = u .
TO1=31=<N(i:
UT (I) * 2- *CTE2 (I) + IS511*.25
i) *;.*ciI + CSE1
Dlfl) *C12 (I) +USE1)/b.D E 1 ( I ) + C S E
1
IF(TSPIN.IE.O„) DEIS |15)=?CIE ITACC. LZ.O . ) CEIN ( 16) =AC
TiiT=D2IN (2)P£I=DEIN <3)v=czi:i i-oVX=V*QCOE (THT) *DCCS (ESI)V 1= 7 * D CC S |T HT ) * CS IK (ESI)VZ=-V*DSI5 (THT)
165
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B < 1] =D EI S j 1
-( JC + VXO*DT)
5 (2} = QEJ -1 Y C + V Y
B {2) -- - ( Z C i- V ZO *Cc ('») = ,' i- . xoE ( 5 ) = V { - 1 \
5 (6) =V2-
B (6) =0.2 |9)=0.RcTU] N
ESC
164
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3
1001 5 C
i
. i ...
'
{ A , X , 9 , S
I 5 J 1 = 1 , i
XX (I) = AX |I)-e (J)CCKTINU2
iX IRAPGLAIE
CAL
EX2HAPGLA1E :CVASIAfiC2
CALL VMOLFF (?,A1,S,9,9,9 ,9,? AT, 9, 12)
CALL /MDLFF (A ,5*1,9,3,9 ,9,9, A EAT, ; ,I3)
DC 15 J 1=1,9CC 100 0=1,3? ? 1 1 . J ) = A £A T 1 1 .01 + C 1 1 . J )?? [I, J) = A£AT (I ,0CONTINUEcc :i xi:j ue
E C 8 a H >.:,z .i : X A 1 s I C ES
X1 = XX ( 1)( 2
)
x.; = xx;5):c u = x x ;
-•i
X5 = XX 5)X 6 = X X ( 6 i
D =DS0 3T (X1**2+X2**2+X3* .
.... .-/: (11**2+X2**2)E2=X1»XU *X2*XS *X3*X6E1=XU+X6E2=X5+ X6E3=XU-X5EU=D1*D1+2.*D*CC3=X1*X3D<J=X2*X3D 5 = ( X 1 * XC6=X2*E3D7=X1*E3
* (2. *E*C*E1*E1*E 1- (X1*X1*EH-X2*X2*£2) * (3 . *D 1*D 1 + D*D)» (2. * C * D * E 1 * C 1*S 2- ( X 1*X1*E1+X2*X2*E2) * (j.*D1*=0 1+D*C)1*E1+X2*X2*E2)* (D*D-X3*X3)
1 m *X 1*21-o* c *c 1 *i
-d*d*di*di+x2*x2*eiO
DC 250 1=1,6CC 200 0=1,9
1 1, J) =0.20 CCSTISUE25C ccxir.i us
H (1,1) =Xa (1,2) =xH (1,3 =-a < 2 , i ) = dK (2,2) =DK (2,3) =DH (2,4) =XH (2,3 = XH ( u, 6) =Da |3,1}=-H (3,2) =XH (<.,1) =CH (4,2) =£a (4,3) =(
a (u,5) =|
H (5, 1) = X
H (5,2) =X
1*X3/ |E1*C*D)2*X3/ (C1*E*C)C 1/ (D*C)2 / I i D * * 5 3) )
J))
2/ ((0**5) * (C1«/((C**3l*(C15/( D**5) *D1)1 *Sl *X3/ (C*C*D*D1
)
2*X2*X2/ (l*C*D*0 1)1*X3/ |E*D*C)X2/ (d*C1)1/ (D 1 *C1)fc/ ((E**3) MCI"'; ) )
// ( (0**3) * (C1**4J )
X1*X2*J3*E3)/((D**3-X1*X2)/ IC*E1*01)X 1 * X 2 ) / (
G ' D 1 - D !)
1/D
* |E1**2) )
16 5
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5035C
:0
U5C
5CC
55C
50065C
H (5, 3) =X3/DH(c,1)=(C*0*XU-D2*X1)/(D*H<6,2)=(£*D*X5-E2*X2)/(C*D*DH 1 6 , 3 ) =
, E *
.
-.. |
I . • ) = A 1 / J
H (6,5) = X2/DU (6 , S) = X3/D
CO 350 1=1,9OC 30 j J=1,bHI II, J] =H |J,I)
" J :;
E
compute kalman saih matrix
CAII / ;iu iff ( ??,::, ,.:
, ;,o , j ,i ,?h:, ^ ,i-)
CALL VXUL5F (H ,£HI , 6 , S,6 ,6 ,9 ,H EET,6,I 5)
DC «50 1=1.6C C 4 G J = 1 , oi li, j) =;:;hi (i ,j) f s u ,0)CCKTIMUECCNXIbJ UE
INVERT Y » A T S
I
i
CALL LISV1F(X,6,6,XI!>7,0, *ORK,IER) '
CALL VflULFF {PHI, YIJiV ,9,5 , 6,9 ,£,G,9,I6)
UPDATE SI ATE VECICS
KH(1)=-DA1AS2<X3,E1)HH<2)=({X1*X1*X3*XU)+(X2*X2*X3*X5) + (E1*E1 :!=X3*
I / i 1 -
'
I
HK | j| =DA1AN2 (X2 , XI)HH(aj = ({-X1*X2*X4)+| i * X 2 'X5))/(G*D1*£1)HH (5) =CH S ( 6 ) = D 2 / C
DO 5 CO 1 = 1,6C2 (I)=Z (I)-HH (I)CONTINUE
call vhuiff (G , :
:
DC 550 1=1,9X (I)=XX (I) *-DX |I)
[SUE
,1,9,6,DX,9,I7)
UFEATE CCVARIAKCE
CALL VHUIFF(G,H,9,6,S,9,6 ,GH,9,I8)
DC £50 1=1,9CC 6 00 J =1,9P <I,Jj =DEI (ICCSTINOEC S TIN UE
i)-GH |l ,0)
CALL VMOIF? (F,EP,S,9,9,9 ,'
RF.TURHEKE
166
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IJiFLIC
V=£AT*;. = c * : r
s 1 = DEXE2=C£X
V 11 = 7/V 1 2 = 7 /
m )= < ,'
I 2 2 -- / •
7 2i=V/V33 = V
Q 11 = 7 1
£12=V1; 15= ; 1
C22=V2Q23=V2C32=VJ
TIN; PI! C|
C,DT,f 1,F2,i 3,Q I 1 , * 12 ,Q 1 3 , J.II SEAI^E (A-H ,0-2)
*2
Ff-fl)F {
- 2 . * A
)
(C**3j
(C**2)
1 * [1.-S2+2.*A+ 1 2 . / 3 . ; * ( A * * 3 ) - 2 .-
2 * { H 2 + 1 . -.
•- I + 2 . * A * S 1 - 2 . * A +A * A
)
J* [ 1.-52- 2- *A* HI)'2* {t * • 1 - ;
- - - 2 + 2 . * \ )
13* |£2+1 .-2.*£1)3* [ 1.-52}
:1)
ml-EKE
l.+E1)/(C**2)-t 1)/C
167
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IOC
20i
/*
/*
SCEaOOTIX I ST SI (X,M, AVGIMPLICIT 3 E .M J c
tA - .-
,0-
£ E JiS 10 S .((50)
sue=o.DC ICO 1=1,
M
so2=sa«+x (i)CCSTIN DESVG=5U 2/ 8K£ t) .".
i
£ C 2 20 1=1,3I)-.A vG) **2
C C :. : . I E
3 M S =0 £ S U iJ 2 / A li]
EETUfiNEND
.5YSIN OD *
16S
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•
LIST OF REFERENCES
1 . uriam, :
. L , tics , IVi] ;,
19 71.
2 . Etkin , B . , Dynamics of Atmos ph eric Fligh t , W i 1 ey , 1
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