-- PREFACE xvii -- CONTRIBUTORS xxiii -- PART I FILTERING -- 1. Angle-Only Filtering in Three Dimensions 3 / Mahendra Mallick, Mark Morelande, Lyudmila Mihaylova, Sanjeev Arulampalam, 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 for Tracking Applications 43 / Amadou Gning, Lyudmila Mihaylova, Fahed Abdallah, and Branko Ristic / / 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 Inference Using 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 Problem 67 -- 2.9 Conclusions 71 -- 3. Bayesian Multiple Target Filtering Using Random Finite Sets 75 / Ba-Ngu Vo, Ba-Tuong Vo, and Daniel Clark -- 3.1 Introduction 75 -- 3.2 Overview of the Random Finite Set Approach to Multitarget Filtering 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 Multiple Model 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 Tracking 165 / 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 Surveillance 203 / Michael Mertens, Michael Feldmann, Martin Ulmke, and Wolfgang Koch -- 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 Hypothesis Density Filter 243 -- 7. Performance Bounds for Target Tracking: Computationally Efficient 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 Sensors 271 -- 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 Stochastic Context-Free Grammars 395 -- 10.4 Trajectory Modeling and Inference Using Reciprocal Processes (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 - A Stochastic Control Approach 417 / Vikram Krishnamurthy -- 11.1 Introduction 417 -- 11.2 Problem Formulation 422 -- 11.3 Structural Results and Lattice Programming for Micromanagement 431 -- 11.4 Radar Scheduling for Maneuvering Targets Modeled as Jump Markov Linear System 437 -- 11.5 Summary 444 -- 12. Sensor Management for Large-Scale Multisensor-Multitarget Tracking 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 Networks for 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 Model 532 -- 13.4 Hybrid Message Passing Algorithm for Classification 536 -- 13.5 Numerical Experiments 537 -- 13.6 Concluding Remarks 544 -- 14. Evaluating Multisensor Classification Performance with Bayesian 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 Performance Evaluation 560 -- 14.4 Summary and Continuing Questions 564 -- 15. Detection and Estimation of Radiological Sources 579 / 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 with Applications 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 Surveillance Systems 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.

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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.

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