Locating, Classifying and Countering Agile Land Vehicles: With Applications to Command Architectures

300 Pages·2016·9.31 MB·English

by  David D. Sworder

David D. Sworder · John E. Boyd
Locating,
Classifying and
Countering
Agile Land
Vehicles
With Applications to Command
Architectures
Locating, Classifying and Countering Agile
Land Vehicles

David D. Sworder • John E. Boyd
Locating, Classifying and
Countering Agile Land
Vehicles
With Applications to Command Architectures
123
DavidD.Sworder JohnE.Boyd
DepartmentofECE,UCSD CubicDefenseApplications
UniversityofCalifornia,SanDiego SanDiego,CA,USA
LaJolla,CA,USA
ISBN978-3-319-19430-1 ISBN978-3-319-19431-8 (eBook)
DOI10.1007/978-3-319-19431-8
LibraryofCongressControlNumber:2015944259
SpringerChamHeidelbergNewYorkDordrechtLondon
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Preface
Inmanymoderntrackingapplications,targetdetectionandplacementareperformed
tosupportsomespecificdecisionoraction.Estimatesoftargetpositionandmotion
will be used for some command purpose, perhaps beginning with determining
target intent and then selecting an appropriate course of action like intercepting
or otherwise countering the target. The challenge is to construct an architecture
that uses noisy data sequences of indicated location not only to derive estimated
position, velocity, and attitude of the target but also to provide the information
required to support effective command functions. This book focuses on command
architectures—estimation and information structures that can effectively inform
usefulcommanddecisions.Thetargetengagementswehaveinviewalmostalways
demand that a command architecture function in real time. Frequently, there are
strictlimitsontheinterrogationandcountermeasureresourcesexpended.Command
architectures considerably expand the character and complexity of target trackers,
whether relatively simple, like alpha-beta algorithms, or more sophisticated, like
extendedKalmanfilterandmultiple-modelestimators.
We contrast the performance of command architectures based upon a single-
model representation of motion with those based on multiple-model approaches
to the same problem. The latter start with the same raw measurement data sets
as the former but use more sophisticated situation assessment tools to facilitate
the simultaneous tasks of locating, classifying, and countering. Our purpose is to
highlight the advantages a hybrid problem formulation provides for the decision-
makingcommandalgorithms.
Weexploreseveralprototypicalengagements.Ineach,wedeterminethequality
ofasingle-modelalgorithmandexploreitssensitivitieswhenthetargetisuncoop-
erativeandgiventoabruptchangesinkinematicstate.Wecontrasttheestimator’s
actual performance with that predicted on the basis of the model. We then turn
to a hybrid algorithm that integrates an element of situation awareness with the
same kinematic data set found deficient in the single-model algorithm. Again the
sensitivitiesarepresented.Thecomparisonisdonewithaviewtotherequirements
ofacommandarchitectureandtheeffectivenessofindicateddecisions.
v
vi Preface
The book is intended for engineers who are tasked with developing command
algorithms for applications that must operate in highly variable and ambiguous
environments.Thecasespresentedandthearchitecturesconsideredwillalsoserve
to illustrate the crucial dependence of effective command on model complexity,
modelrealism,andestimationqualityforreal-worldplantsandsystems.
We have included, for the interested reader, detailed analytical developments
in an appendix. The appendix also provides an example of a step-by-step imple-
mentation of the hybrid estimation algorithm. To function in a wildly changing
environment,thehybridalgorithmisnecessarilyquiteintricate.Forthearchitectures
in this book, we have chosen to work in 2D (motion in the plane) and, to make
the engagements concrete, have referred to the target as a ground object. These
constraintscanbeeasilyrelaxed.
Theauthors’earlierbook,EstimationProblemsinHybridSystems(Cambridge,
1999),exploredthemathematicalstructureofoneformofhybridestimation.This
book utilizes that approach in an assortment of different engagement applications.
Thestrictmathematicalbasisoftrackingalgorithmsisoftenviolatedinapplications.
Thisbookexplainsandillustratestheengineeringjudgmentsthatmustbeemployed
whenthedevelopmentalhypothesesareviolated.
This work contrasts the performance of command architectures in several par-
ticularengagements.Ineachcase,thealgorithmsarepresentedthesamekinematic
data stream. The idiosyncratic engagements are chosen to illustrate the distinctive
sensitivities of alternative modeling approaches rather than to provide a detailed
performancecomparisonforaspecificapplication.
The authors wish to thank Cubic Defense Applications for their support in
publishingthisbookand,inparticular,forthegraphicsandeditingsupportoftheir
technicalpublicationsdepartment.
LaJolla,CA,USA DavidD.Sworder
SanDiego,CA,USA JohnE.Boyd
February2015
Contents
SymbolsandNotationalConventions.......................................... xi
1 AModelforTrackingandClassification ................................ 1
1.1 IntroductiontotheProblem.......................................... 1
1.2 AHybridStateModelofaManeuveringTarget.................... 8
1.2.1 TheModalState ............................................ 8
1.2.2 KinematicState............................................. 13
1.3 TheMeasurementProcess .......................................... 19
1.3.1 Regime-StateMeasurement................................ 19
1.3.2 Kinematic-StateMeasurement............................. 21
1.4 StateEstimatesUsingtheGaussian-SumDistribution............. 22
1.5 AssuranceRegions................................................... 25
1.6 TrackerArchitecture ................................................. 30
1.6.1 TheConditionalDistributionoftheTargetState.......... 30
1.6.2 TheProjectedDistribution................................. 32
1.7 OrganizationoftheBook ............................................ 33
1.8 Conclusion............................................................ 34
2 TargetLocationUsingtheExtendedKalmanFilter.................... 37
2.1 Introduction........................................................... 37
2.2 TheExtendedKalmanFilter......................................... 40
2.3 ExamplesoftheEKF-Tracker....................................... 42
2.3.1 TheCooperativeTarget..................................... 42
2.3.2 TheUncooperativeTarget.................................. 45
2.4 Range-OnlyTracking ................................................ 47
2.4.1 TheEKF-TrackeronaRoadway .......................... 47
2.4.2 GDOPandTrackerAccuracy.............................. 51
2.4.3 TheEKF-TrackerIncludingtheNorthNode.............. 52
2.4.4 Map-EnhancedTracking................................... 54
2.5 TrackinginthePresenceofSpoofing ............................... 57
2.5.1 RegistrationErrors.......................................... 57
vii
viii Contents
2.5.2 FlaggingRegistrationErrorsUsingAssuranceRegions.. 58
2.5.3 Map-EnhancedFlagging ................................... 63
2.6 Conclusions........................................................... 64
3 TrackinganAgileTarget .................................................. 67
3.1 Introduction........................................................... 67
3.2 Multi-regimeEngagementModel................................... 69
3.2.1 KinematicModel ........................................... 69
3.2.2 ModalModel................................................ 69
3.2.3 KinematicMeasurement ................................... 71
3.2.4 RegimeMeasurement ...................................... 71
3.3 TheUni-ModelTracker.............................................. 73
3.3.1 TheEngagement............................................ 73
3.3.2 TheEKF-Tracker........................................... 74
3.4 TheMulti-modelTracker............................................ 78
3.4.1 TheEngagement............................................ 78
3.4.2 TheGWEAlgorithm....................................... 80
3.4.3 TheGWE-Tracker.......................................... 82
3.5 Conclusion............................................................ 88
4 InterceptandTargetPrediction........................................... 91
4.1 Introduction........................................................... 91
4.2 TheEKF ............................................................. 93
4.2.1 EKF-Tracker................................................ 93
4.2.2 EKF-Predictor .............................................. 94
4.3 TheYŒk(cid:2)-GWE ..................................................... 98
4.3.1 YŒk(cid:2)-GWE-Tracker........................................ 98
4.3.2 YŒk(cid:2)-GWE-Predictor ...................................... 100
4.4 TheGŒk(cid:2)-GWE ...................................................... 104
4.4.1 GŒk(cid:2)-GWE-Tracker......................................... 104
4.4.2 GŒk(cid:2)-GWE-Predictor....................................... 107
4.5 Conclusion............................................................ 109
5 ClassificationandTargetTempo.......................................... 113
5.1 Introduction........................................................... 113
5.2 TheEngagement...................................................... 118
5.3 GŒk(cid:2)-Tracking:MarkovorGamma.................................. 120
5.4 TargetClassification.................................................. 124
5.4.1 FriendlyorHostile.......................................... 124
5.4.2 CommonLifetimesandRegimes.......................... 126
5.4.3 DistinctiveKinematics,Range-BearingSensors.......... 134
5.5 Conclusion............................................................ 136
6 AssuranceRegionsonaRoadGrid ...................................... 141
6.1 Introduction........................................................... 141
6.2 TheEngagement...................................................... 143
6.2.1 KinematicModel ........................................... 143
6.2.2 Measurements............................................... 146
Contents ix
6.3 TheEKF ............................................................. 148
6.3.1 NominalEKF .............................................. 148
6.3.2 Pseudo-NoiseAugmentation............................... 150
6.3.3 TrackerContrasts........................................... 152
6.4 TheMap-EnhancedGWE .......................................... 157
6.4.1 AlgorithmOverview........................................ 157
6.4.2 TheGWE-Tracker.......................................... 163
6.5 Conclusions........................................................... 173
7 ActionWindowswithResourceLimits................................... 177
7.1 Introduction........................................................... 177
7.2 ActionWindowswithaGridMap................................... 180
7.2.1 TheEngagement............................................ 180
7.2.2 EKF-Illuminator............................................ 182
7.2.3 GWE-Illuminator........................................... 185
7.3 BallisticFireControl................................................. 190
7.3.1 TheEngagement............................................ 190
7.3.2 EKFFireControl........................................... 191
7.3.3 GWEFireControl.......................................... 193
7.4 Conclusions........................................................... 198
8 SerendipitousEventsinTrackingandClassification................... 201
8.1 SystemSpecifications................................................ 201
8.2 TheVisibleTarget.................................................... 203
8.2.1 TheEngagementModel.................................... 203
8.2.2 OmnipresentEKF .......................................... 204
8.3 TheVeiledTarget..................................................... 208
8.3.1 TheEngagement............................................ 208
8.3.2 BlindedEKF ............................................... 209
8.3.3 GWE ....................................................... 211
8.3.4 AlgorithmContrasts........................................ 217
8.4 TheVeiledTargetwithMoreAmbiguousPara-Measurements.... 219
8.4.1 TheEngagement............................................ 219
8.4.2 BlindedEKF ............................................... 220
8.4.3 GWE ....................................................... 222
8.5 Conclusions........................................................... 233
9 DeceivingaClassifier....................................................... 235
9.1 Introduction........................................................... 235
9.2 TheEngagement...................................................... 237
9.3 AnEKFClassifier.................................................... 238
9.3.1 NoSpoofing................................................. 238
9.3.2 Spoofing..................................................... 240
9.3.3 ABeaconAssist ............................................ 241
9.4 AHybridClassifier................................................... 242
9.4.1 Spoofing..................................................... 242
9.4.2 ABeaconAssist ............................................ 247