Integrated Tracking, Classification, and Sensor Management: Theory and Applications

A unique guide to the state of the art of tracking, classification, and sensor management

This book addresses the tremendous progress made over the last few decades in algorithm development and mathematical analysis for filtering, multi–target multi–sensor tracking, sensor management and control, and target classification. It provides for the first time an integrated treatment of these advanced topics, complete with careful mathematical formulation, clear description of the theory, and real–world applications.

Written by experts in the field, Integrated Tracking, Classification, and Sensor Management provides readers with easy access to key Bayesian modeling and filtering methods, multi–target tracking approaches, target classification procedures, and large scale sensor management problem–solving techniques. Features include:

An accessible coverage of random finite set based multi–target filtering algorithms such as the Probability Hypothesis Density filters and multi–Bernoulli filters with focus on problem solving A succinct overview of the track–oriented MHT that comprehensively collates all significant developments in filtering and tracking A state–of–the–art algorithm for hybrid Bayesian network (BN) inference that is efficient and scalable for complex classification models New structural results in stochastic sensor scheduling and algorithms for dynamic sensor scheduling and management Coverage of the posterior Cramer–Rao lower bound (PCRLB) for target tracking and sensor management Insight into cutting–edge military and civilian applications, including intelligence, surveillance, and reconnaissance (ISR)

With its emphasis on the latest research results, Integrated Tracking, Classification, and Sensor Management is an invaluable guide for researchers and practitioners in statistical signal processing, radar systems, operations research, and control theory.

PREFACE xvii

CONTRIBUTORS xxiii

PART I FILTERING

1. Angle–Only Filtering in Three Dimensions 3
Mahendra Mallick, Mark Morelande, Lyudmila Mihaylova, SanjeevArulampalam, and Yanjun Yan

1.1 Introduction 3

1.2 Statement of Problem 6

1.3 Tracker and Sensor Coordinate Frames 6

1.4 Coordinate Systems for Target and Ownship States 7

1.5 Dynamic Models 9

1.6 Measurement Models 14

1.7 Filter Initialization 15

1.8 Extended Kalman Filters 17

1.9 Unscented Kalman Filters 19

1.10 Particle Filters 23

1.11 Numerical Simulations and Results 28

1.12 Conclusions 31

2. Particle Filtering Combined with Interval Methods forTracking Applications 43
Amadou Gning, Lyudmila Mihaylova, Fahed Abdallah, and BrankoRistic

2.1 Introduction 43

2.2 Related Works 44

2.3 Interval Analysis 46

2.4 Bayesian Filtering 51

2.5 Box Particle Filtering 52

2.6 Box Particle Filtering Derived from the Bayesian InferenceUsing a Mixture of Uniform Probability Density Functions 56

2.7 Box–PF Illustration over a Target Tracking Example 65

2.8 Application for a Vehicle Dynamic Localization Problem67

2.9 Conclusions 71

3. Bayesian Multiple Target Filtering Using Random FiniteSets 75
Ba–Ngu Vo, Ba–Tuong Vo, and Daniel Clark

3.1 Introduction 75

3.2 Overview of the Random Finite Set Approach to MultitargetFiltering 76

3.3 Random Finite Sets 81

3.4 Multiple Target Filtering and Estimation 85

3.5 Multitarget Miss Distances 91

3.6 The Probability Hypothesis Density (PHD) Filter 95

3.7 The Cardinalized PHD Filter 105

3.8 Numerical Examples 111

3.9 MeMBer Filter 117

4. The Continuous Time Roots of the Interacting MultipleModel Filter 127
Henk A.P. Blom

4.1 Introduction 127

4.2 Hidden Markov Model Filter 129

4.3 System with Markovian Coefficients 136

4.4 Markov Jump Linear System 141

4.5 Continuous–Discrete Filtering 149

4.6 Concluding Remarks 154

PART II MULTITARGET MULTISENSOR TRACKING

5. Multitarget Tracking Using Multiple Hypothesis Tracking165
Mahendra Mallick, Stefano Coraluppi, and Craig Carthel

5.1 Introduction 165

5.2 Tracking Algorithms 166

5.3 Track Filtering 170

5.4 MHT Algorithms 179

5.5 Hybrid–State Derivations of MHT Equations 180

5.6 The Target–Death Problem 185

5.7 Examples for MHT 186

5.8 Summary 189

6. Tracking and Data Fusion for Ground Surveillance203
Michael Mertens, Michael Feldmann, Martin Ulmke, and WolfgangKoch

6.1 Introduction to Ground Surveillance 203

6.2 GMTI Sensor Model 204

6.3 Bayesian Approach to Ground Moving Target Tracking 209

6.4 Exploitation of Road Network Data 222

6.5 Convoy Track Maintenance Using Random Matrices 234

6.6 Convoy Tracking with the Cardinalized Probability HypothesisDensity Filter 243

7. Performance Bounds for Target Tracking: ComputationallyEfficient Formulations and Associated Applications 255
Marcel Hernandez

7.1 Introduction 255

7.2 Bayesian Performance Bounds 258

7.3 PCRLB Formulations in Cluttered Environments 262

7.4 An Approximate PCRLB for Maneuevring Target Tracking 269

7.5 A General Framework for the Deployment of Stationary Sensors271

7.6 UAV Trajectory Planning 294

7.7 Summary and Conclusions 305

8. Track–Before–Detect Techniques 311
Samuel J. Davey, Mark G. Rutten, and Neil J. Gordon

8.1 Introduction 311

8.2 Models 318

8.3 Baum Welch Algorithm 327

8.4 Dynamic Programming: Viterbi Algorithm 331

8.5 Particle Filter 334

8.6 ML–PDA 337

8.7 H–PMHT 341

8.8 Performance Analysis 347

8.9 Applications: Radar and IRST Fusion 354

8.10 Future Directions 357

9. Advances in Data Fusion Architectures 363
Stefano Coraluppi and Craig Carthel

9.1 Introduction 363

9.2 Dense–Target Scenarios 364

9.3 Multiscale Sensor Scenarios 368

9.4 Tracking in Large Sensor Networks 370

9.5 Multiscale Objects 372

9.6 Measurement Aggregation 378

9.7 Conclusions 383

10. Intent Inference and Detection of Anomalous Trajectories:A Metalevel Tracking Approach 387
Vikram Krishnamurthy

10.1 Introduction 387

10.2 Anomalous Trajectory Classification Framework 393

10.3 Trajectory Modeling and Inference Using StochasticContext–Free Grammars 395

10.4 Trajectory Modeling and Inference Using ReciprocalProcesses (RP) 403

10.5 Example 1: Metalevel Tracking for GMTI Radar 406

10.6 Example 2: Data Fusion in a Multicamera Network 407

10.7 Conclusion 413

PART III SENSOR MANAGEMENT AND CONTROL

11. Radar Resource Management for Target Tracking AStochastic Control Approach 417
Vikram Krishnamurthy

11.1 Introduction 417

11.2 Problem Formulation 422

11.3 Structural Results and Lattice Programming forMicromanagement 431

11.4 Radar Scheduling for Maneuvering Targets Modeled as JumpMarkov Linear System 437

11.5 Summary 444

12. Sensor Management for Large–Scale Multisensor–MultitargetTracking 447
Ratnasingham Tharmarasa and Thia Kirubarajan

12.1 Introduction 447

12.2 Target Tracking Architectures 451

12.3 Posterior Cram´er Rao Lower Bound 452

12.4 Sensor Array Management for Centralized Tracking 458

12.5 Sensor Array Management for Distributed Tracking 473

12.6 Sensor Array Management for Decentralized Tracking 489

12.7 Conclusions 507

PART IV ESTIMATION AND CLASSIFICATION

13. Efficient Inference in General Hybrid Bayesian Networksfor Classification 523
Wei Sun and Kuo–Chu Chang

13.1 Introduction 523

13.2 Message Passing: Representation and Propagation 526

13.3 Network Partition and Message Integration for Hybrid Model532

13.4 Hybrid Message Passing Algorithm for Classification 536

13.5 Numerical Experiments 537

13.6 Concluding Remarks 544

14. Evaluating Multisensor Classification Performance withBayesian Networks 547
Eswar Sivaraman and Kuo–Chu Chang

14.1 Introduction 547

14.2 Single–Sensor Model 548

14.3 Multisensor Fusion Systems Design and PerformanceEvaluation 560

14.4 Summary and Continuing Questions 564

15. Detection and Estimation of Radiological Sources579
Mark Morelande and Branko Ristic

15.1 Introduction 579

15.2 Estimation of Point Sources 580

15.3 Estimation of Distributed Sources 590

15.4 Searching for Point Sources 599

15.5 Conclusions 612

PART V DECISION FUSION AND DECISION SUPPORT

16. Distributed Detection and Decision Fusion withApplications to Wireless Sensor Networks 619
Qi Cheng, Ruixin Niu, Ashok Sundaresan, and Pramod K.Varshney

16.1 Introduction 619

16.2 Elements of Detection Theory 620

16.3 Distributed Detection with Multiple Sensors 624

16.4 Distributed Detection in Wireless Sensor Networks 634

16.5 Copula–Based Fusion of Correlated Decisions 645

16.6 Conclusion 652

17. Evidential Networks for Decision Support in SurveillanceSystems 661
Alessio Benavoli and Branko Ristic

17.1 Introduction 661

17.2 Valuation Algebras 662

17.3 Local Computation in a VA 668

17.4 Theory of Evidence as a Valuation Algebra 672

17.5 Examples of Decision Support Systems 685

References 702

Index 705

MAHENDRA MALLICK, PhD, is Principal Research Scientist atthe Propagation Research Associates, Inc. A senior member of theIEEE, he has served as the associate editor–in–chief of the onlinejournal of the International Society of Information Fusion(ISIF).

VIKRAM KRISHNAMURTHY, PhD, holds the Canada ResearchChair in Statistical Signal Processing at The University of BritishColumbia. He is an IEEE Fellow and Editor–in–Chief of the IEEEJournal of Selected Topics in Signal Processing.

BA–NGU VO, PhD, is Professor and Chair of Signals andSystems in the Department of Electrical and Computer Engineering atCurtin University in Western Australia. He is Associate Editor forIEEE Transactions on Aerospace and Electronic Systems.