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Multitarget-Multisensor Tracking Applications and Advances

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ISBN-10: 0890065179

ISBN-13: 9780890065174

Edition: 1991

Authors: Yaakov Bar-Shalom

List price: $168.00
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Compiles the latest techniques for those who design advanced systems for tracking, surveillance and navigation. This second volume expands upon the first with 11 new chapters. The text includes pertinent contributions from leading international experts in this field.
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Book details

List price: $168.00
Copyright year: 1991
Publisher: Artech House, Incorporated
Binding: Hardcover
Pages: 442
Size: 6.00" wide x 9.50" long x 1.25" tall
Weight: 1.782
Language: English

Prefacep. xi
Introductionp. xiii
Design of a Tracking Algorithm for an Advanced ATC Systemp. 1
Introductionp. 1
The Hadamard Projectp. 1
Characteristics of the New ATC Systemp. 2
Tracking Accuracy Requirementsp. 3
Aircraft Motion Modelingp. 5
Modeling Influence on Estimation Qualityp. 5
Aircraft Maneuver Modelingp. 7
Tracking Algorithmp. 10
Multiple Model Formulation of Aircraft Trajectoryp. 10
The Interacting Multiple Model Algorithmp. 14
Evaluation of the IMM for Air Traffic Simulationsp. 16
Algorithms Based on Exact Maneuver Modelingp. 17
Algorithms Based on Approximate Maneuver Modelingp. 21
Algorithm Selection for Hadamard Tracking Systemp. 26
Conclusionp. 28
Referencesp. 28
Design of a Multisensor Tracking System for Advanced Air Traffic Controlp. 31
Introductionp. 31
Multisensor Tracking Modulesp. 32
Coordinate Transformationp. 33
Track Maintenance (Continuation)p. 33
Track Deletionp. 33
Measurement Memorizationp. 34
Track Formation (Initiation)p. 34
Track Mergingp. 34
Systematic Error Estimationp. 34
Aircraft Track Selectionp. 35
Synchronizationp. 35
Bayesian Track Continuationp. 35
Systematic Error Estimationp. 38
Evaluation of the Tracking Performancep. 41
Summary and Conclusionsp. 48
Jumpdif Track Maintenance Equations in the Horizontal Directionp. 48
Interaction Step of Generalized IMMp. 49
EKF Time Extrapolation Equationsp. 51
PDA-Based Measurement Update Equationsp. 53
Output Calculationsp. 56
Joint Tracking and Sensors' Systematic Error Estimationp. 56
Extended Kalman Filterp. 58
The Single-Sensor Situationp. 59
The Multisensor Situationp. 59
Systematic Error Estimation After Convergencep. 60
Referencesp. 62
Passive Sensor Data Fusion and Maneuvering Target Trackingp. 65
Introductionp. 65
The Application: Passive Sensor Data Fusionp. 66
A Hybrid Model Based Algorithm: The IMMPDA Filterp. 69
Hybrid Systemsp. 69
Hybrid Filtersp. 70
Target Motion Modelsp. 75
First Set of Modelsp. 75
Second Set of Modelsp. 76
Third Set of Modelsp. 78
Simulation Resultsp. 79
Parameter Valuesp. 81
Single Model Referencep. 82
Peformance Analysisp. 82
Summary and Conclusionp. 91
Referencesp. 91
Tracking Splitting Targets in Clutter by Using an Interacting Multiple Model Joint Probabilistic Data Association Filterp. 93
Introductionp. 93
The Approachp. 94
The Models for the Splitting and Their State Estimationp. 96
The Transitions Between the Modelsp. 96
The "Just Split" Modelp. 97
The "Split" Modelp. 99
The Interaction Between the Modelsp. 102
Simulation Resultsp. 103
Conclusionp. 110
Referencesp. 110
Precision Tracking of Small Extended Targets with Imaging Sensorsp. 111
Introductionp. 111
Extraction of Measurements from an Imaging Sensorp. 113
Modeling the Imagep. 114
Estimation of the Centroidp. 115
The Offset Measurement from Image Correlationp. 117
Application to a Gaussian Plume Targetp. 118
Precision Target Tracking of the Image Centroidp. 120
Filter with White Measurement Noise Modelp. 121
Filter with Autocorrelated Noise Modelp. 122
Simulation Resultsp. 124
Tracking Crossing Targets with FLIR Sensorsp. 125
Backgroundp. 125
Problem Formulationp. 126
The State Estimationp. 130
Simulation Results for Crossing Targetsp. 137
Derivations for the Centroid Estimatep. 140
The Offset Measurement from Image Correlationp. 143
Evaluation of the "Image-Mixing" Parameterp. 146
Referencesp. 147
A System Approach to Multiple Target Trackingp. 149
Introductionp. 149
Measurement Pattern Optimizationp. 152
Waveform Optimizationp. 160
Resolutionp. 165
Fundamental Limits in Multiple Target Trackingp. 173
Probabilities of Resolution and Data Associationp. 179
Referencesp. 180
Performance Analysis of Optimal Data Association with Applications to Multiple Target Trackingp. 183
Introductionp. 183
Problem Statementp. 187
Probability of Correct Associationp. 190
Effects of Misassociationp. 194
Effects of Extraneous Objectsp. 213
Application to Multitarget Trackingp. 218
Conclusionsp. 227
Some Spherical Integralsp. 228
Conditional Gaussian Distributionsp. 230
Referencesp. 233
Mutitarget Tracking with an Agile Beam Radarp. 237
Introductionp. 237
Performance Predictionp. 238
Analytic Methods for Predicting Track Accuracyp. 239
Analytic Methods for Predicting Correlation (Association) Performancep. 240
Monte Carlo Simulationp. 241
Detection: Observation Generation and Processingp. 242
Enhancing Detection and Measurement Peformancep. 242
Reducing the Effects of Jet Engine Modulationp. 243
Radar Resource Allocationp. 244
Choice of Optimal TOTp. 245
Global Allocation Strategyp. 249
Determining Task Figures of Meritp. 250
Utility Theory Allocationp. 251
Expert System Allocationp. 254
Other Allocation Issuesp. 257
Typical Allocation Examplep. 258
Filtering and Predictionp. 260
Choice of Tracking Coordinates and Statesp. 260
Target Maneuver Modeling and Detectionp. 261
Modified Spherical Coordinatesp. 261
Data Associationp. 262
Conventional Data Associationp. 262
Multiple Hypothesis Trackingp. 263
Joint Probabilistic Data Associationp. 264
Group Trackingp. 264
Other Implementation Issuesp. 265
Other Future System Issuesp. 265
Track Confirmation for Low-Observable Targetsp. 265
Radar as Part of Multiple Sensor Systemp. 266
Conclusionp. 267
Referencesp. 267
Autonomous Navigation with Uncertain Reference Points Using the PDAFp. 271
Introductionp. 271
Autonomous Navigation Without Landmark Recognitionp. 272
Discrete-Time State and Observation Modelsp. 272
Notationp. 276
Measurement Validation Testp. 277
Formulation of the Autonomous Navigation Filterp. 278
Autonomous Navigation with landmark Recognitionp. 282
Inclusion of Bayesian Recognition Informationp. 282
Use of Uncertain Recognition Informationp. 286
Inclusion of a Detected Landmark Identity Classificationp. 302
Simulation Resultsp. 314
Summary and Conclusionsp. 317
Calculation of the Association Probabilities for a Filter Using a Classifierp. 319
Referencesp. 323
The Sensor Management Imperativep. 325
Introductionp. 325
Establishing the Sensor Management Imperativep. 327
General Discussionp. 328
Effective Use of Limited System Resourcesp. 330
Track Maintenancep. 332
Sensor Fusion and Synergismp. 333
Situation Assessmentp. 334
Support of Specific Goalsp. 335
Adaptive Behavior in Varying Sensing Environmentsp. 336
Summaryp. 336
Sensor Management Approachesp. 336
Architectures for Sensor Managementp. 337
The Macro-Micro Architecturep. 337
Scheduling Techniquesp. 343
Decision-Making Techniquesp. 347
Demonstrations of Sensor Managementp. 363
Demonstration 1p. 365
Demonstration 2p. 372
Demonstration 3p. 376
Demonstration 4p. 378
Demonstration 5p. 385
Conclusionp. 389
Referencesp. 391
Attribute Fusion and Situation Assessment with a Many-Valued Logic Approachp. 393
Introductionp. 393
Aggregation Operatorsp. 395
Conjunction and Propagation Using Triangular Normsp. 395
Disjunction Using Triangular Conormsp. 396
Relationships Between T-Norms and T-Conormsp. 397
Negation Operators and Calculi of Uncertaintyp. 398
Families of T-Norms and T-Conormsp. 400
Linguistic Variables Defined on the Interval [0, 1]p. 402
Example of a Term Set of Linguistic Probabilitiesp. 403
Description of the Experiments and Required Techniquesp. 404
The First Experimentp. 404
The Second Experimentp. 407
Computational Techniquesp. 408
Conclusions on the Theory Sectionp. 411
Summary of the Resultsp. 411
Impact of the Results on Expert System Technologyp. 412
Reasoning with Uncertainty--RUM and RUMrunnerp. 413
Introductionp. 413
Applications of the RUM Technologyp. 415
Tactical and Surveillance Platform Applicationsp. 416
The Airborne Fighter Problemp. 416
The Surveillance Mission Problemp. 419
Summary and Conclusionsp. 419
Properties of T-Norm Operatorsp. 429
Referencesp. 432
Indexp. 435
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