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Multitarget-Multisensor Tracking

Applications and Advances v. 2

By: Yaakov Bar-Shalom (Editor)

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Hardcover | 1 January 1992

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464 Pages

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24.13 x 15.24 x 3.18

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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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Non-Fiction Engineering & Technology Electronics & Communications Engineering Communications Engineering & Telecommunications Radar Technology Radio Technology Transport Technology & Trades Aerospace & Aviation Technology Industrial Chemistry & Manufacturing Technologies


Preface	p. xi
Introduction	p. xiii
Design of a Tracking Algorithm for an Advanced ATC System	p. 1
Introduction	p. 1
The Hadamard Project	p. 1
Characteristics of the New ATC System	p. 2
Tracking Accuracy Requirements	p. 3
Aircraft Motion Modeling	p. 5
Modeling Influence on Estimation Quality	p. 5
Aircraft Maneuver Modeling	p. 7
Tracking Algorithm	p. 10
Multiple Model Formulation of Aircraft Trajectory	p. 10
The Interacting Multiple Model Algorithm	p. 14
Evaluation of the IMM for Air Traffic Simulations	p. 16
Algorithms Based on Exact Maneuver Modeling	p. 17
Algorithms Based on Approximate Maneuver Modeling	p. 21
Algorithm Selection for Hadamard Tracking System	p. 26
Conclusion	p. 28
References	p. 28
Design of a Multisensor Tracking System for Advanced Air Traffic Control	p. 31
Introduction	p. 31
Multisensor Tracking Modules	p. 32
Coordinate Transformation	p. 33
Track Maintenance (Continuation)	p. 33
Track Deletion	p. 33
Measurement Memorization	p. 34
Track Formation (Initiation)	p. 34
Track Merging	p. 34
Systematic Error Estimation	p. 34
Aircraft Track Selection	p. 35
Synchronization	p. 35
Bayesian Track Continuation	p. 35
Systematic Error Estimation	p. 38
Evaluation of the Tracking Performance	p. 41
Summary and Conclusions	p. 48
Jumpdif Track Maintenance Equations in the Horizontal Direction	p. 48
Interaction Step of Generalized IMM	p. 49
EKF Time Extrapolation Equations	p. 51
PDA-Based Measurement Update Equations	p. 53
Output Calculations	p. 56
Joint Tracking and Sensors' Systematic Error Estimation	p. 56
Extended Kalman Filter	p. 58
The Single-Sensor Situation	p. 59
The Multisensor Situation	p. 59
Systematic Error Estimation After Convergence	p. 60
References	p. 62
Passive Sensor Data Fusion and Maneuvering Target Tracking	p. 65
Introduction	p. 65
The Application: Passive Sensor Data Fusion	p. 66
A Hybrid Model Based Algorithm: The IMMPDA Filter	p. 69
Hybrid Systems	p. 69
Hybrid Filters	p. 70
Target Motion Models	p. 75
First Set of Models	p. 75
Second Set of Models	p. 76
Third Set of Models	p. 78
Simulation Results	p. 79
Parameter Values	p. 81
Single Model Reference	p. 82
Peformance Analysis	p. 82
Summary and Conclusion	p. 91
References	p. 91
Tracking Splitting Targets in Clutter by Using an Interacting Multiple Model Joint Probabilistic Data Association Filter	p. 93
Introduction	p. 93
The Approach	p. 94
The Models for the Splitting and Their State Estimation	p. 96
The Transitions Between the Models	p. 96
The "Just Split" Model	p. 97
The "Split" Model	p. 99
The Interaction Between the Models	p. 102
Simulation Results	p. 103
Conclusion	p. 110
References	p. 110
Precision Tracking of Small Extended Targets with Imaging Sensors	p. 111
Introduction	p. 111
Extraction of Measurements from an Imaging Sensor	p. 113
Modeling the Image	p. 114
Estimation of the Centroid	p. 115
The Offset Measurement from Image Correlation	p. 117
Application to a Gaussian Plume Target	p. 118
Precision Target Tracking of the Image Centroid	p. 120
Filter with White Measurement Noise Model	p. 121
Filter with Autocorrelated Noise Model	p. 122
Simulation Results	p. 124
Tracking Crossing Targets with FLIR Sensors	p. 125
Background	p. 125
Problem Formulation	p. 126
The State Estimation	p. 130
Simulation Results for Crossing Targets	p. 137
Derivations for the Centroid Estimate	p. 140
The Offset Measurement from Image Correlation	p. 143
Evaluation of the "Image-Mixing" Parameter	p. 146
References	p. 147
A System Approach to Multiple Target Tracking	p. 149
Introduction	p. 149
Measurement Pattern Optimization	p. 152
Waveform Optimization	p. 160
Resolution	p. 165
Fundamental Limits in Multiple Target Tracking	p. 173
Probabilities of Resolution and Data Association	p. 179
References	p. 180
Performance Analysis of Optimal Data Association with Applications to Multiple Target Tracking	p. 183
Introduction	p. 183
Problem Statement	p. 187
Probability of Correct Association	p. 190
Effects of Misassociation	p. 194
Effects of Extraneous Objects	p. 213
Application to Multitarget Tracking	p. 218
Conclusions	p. 227
Some Spherical Integrals	p. 228
Conditional Gaussian Distributions	p. 230
References	p. 233
Mutitarget Tracking with an Agile Beam Radar	p. 237
Introduction	p. 237
Performance Prediction	p. 238
Analytic Methods for Predicting Track Accuracy	p. 239
Analytic Methods for Predicting Correlation (Association) Performance	p. 240
Monte Carlo Simulation	p. 241
Detection: Observation Generation and Processing	p. 242
Enhancing Detection and Measurement Peformance	p. 242
Reducing the Effects of Jet Engine Modulation	p. 243
Radar Resource Allocation	p. 244
Choice of Optimal TOT	p. 245
Global Allocation Strategy	p. 249
Determining Task Figures of Merit	p. 250
Utility Theory Allocation	p. 251
Expert System Allocation	p. 254
Other Allocation Issues	p. 257
Typical Allocation Example	p. 258
Filtering and Prediction	p. 260
Choice of Tracking Coordinates and States	p. 260
Target Maneuver Modeling and Detection	p. 261
Modified Spherical Coordinates	p. 261
Data Association	p. 262
Conventional Data Association	p. 262
Multiple Hypothesis Tracking	p. 263
Joint Probabilistic Data Association	p. 264
Group Tracking	p. 264
Other Implementation Issues	p. 265
Other Future System Issues	p. 265
Track Confirmation for Low-Observable Targets	p. 265
Radar as Part of Multiple Sensor System	p. 266
Conclusion	p. 267
References	p. 267
Autonomous Navigation with Uncertain Reference Points Using the PDAF	p. 271
Introduction	p. 271
Autonomous Navigation Without Landmark Recognition	p. 272
Discrete-Time State and Observation Models	p. 272
Notation	p. 276
Measurement Validation Test	p. 277
Formulation of the Autonomous Navigation Filter	p. 278
Autonomous Navigation with landmark Recognition	p. 282
Inclusion of Bayesian Recognition Information	p. 282
Use of Uncertain Recognition Information	p. 286
Inclusion of a Detected Landmark Identity Classification	p. 302
Simulation Results	p. 314
Summary and Conclusions	p. 317
Calculation of the Association Probabilities for a Filter Using a Classifier	p. 319
References	p. 323
The Sensor Management Imperative	p. 325
Introduction	p. 325
Establishing the Sensor Management Imperative	p. 327
General Discussion	p. 328
Effective Use of Limited System Resources	p. 330
Track Maintenance	p. 332
Sensor Fusion and Synergism	p. 333
Situation Assessment	p. 334
Support of Specific Goals	p. 335
Adaptive Behavior in Varying Sensing Environments	p. 336
Summary	p. 336
Sensor Management Approaches	p. 336
Architectures for Sensor Management	p. 337
The Macro-Micro Architecture	p. 337
Scheduling Techniques	p. 343
Decision-Making Techniques	p. 347
Demonstrations of Sensor Management	p. 363
Demonstration 1	p. 365
Demonstration 2	p. 372
Demonstration 3	p. 376
Demonstration 4	p. 378
Demonstration 5	p. 385
Conclusion	p. 389
References	p. 391
Attribute Fusion and Situation Assessment with a Many-Valued Logic Approach	p. 393
Introduction	p. 393
Aggregation Operators	p. 395
Conjunction and Propagation Using Triangular Norms	p. 395
Disjunction Using Triangular Conorms	p. 396
Relationships Between T-Norms and T-Conorms	p. 397
Negation Operators and Calculi of Uncertainty	p. 398
Families of T-Norms and T-Conorms	p. 400
Linguistic Variables Defined on the Interval [0, 1]	p. 402
Example of a Term Set of Linguistic Probabilities	p. 403
Description of the Experiments and Required Techniques	p. 404
The First Experiment	p. 404
The Second Experiment	p. 407
Computational Techniques	p. 408
Conclusions on the Theory Section	p. 411
Summary of the Results	p. 411
Impact of the Results on Expert System Technology	p. 412
Reasoning with Uncertainty--RUM and RUMrunner	p. 413
Introduction	p. 413
Applications of the RUM Technology	p. 415
Tactical and Surveillance Platform Applications	p. 416
The Airborne Fighter Problem	p. 416
The Surveillance Mission Problem	p. 419
Summary and Conclusions	p. 419
Properties of T-Norm Operators	p. 429
References	p. 432
Index	p. 435
Table of Contents provided by Syndetics. All Rights Reserved.

Multitarget-Multisensor Tracking

Applications and Advances v. 2

At a Glance

Hardcover

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