Integrated Tracking, Classification, and Sensor Management

Herausgegeben von Mallick, Mahendra; Krishnamurthy, Vikram; Vo, Ba-Ngu

• Gebundenes Buch

Produktbeschreibung

Written by experts, this book covers topics such as Bayesian Filtering, Multi-Target Tracking, Sensor Management, and Classification. The text features four interrelated parts: filtering, multi-target multi-sensor tracking, sensor management and control, and target classification. These areas are important to statistical signal processing, radar systems, operations research, and control theory. They include the intelligence, surveillance, and reconnaissance (ISR) applications of interest in civilian and military circles. The contents emphasize careful mathematical formulation, clear description of theory, and real-world applications in these areas, making them readily accessible to researchers and practitioners.

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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.

Produktdetails

• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 736
• Erscheinungstermin: Dezember 2012
• Englisch
• Abmessung: 240mm x 161mm x 44mm
• Gewicht: 1261g
• ISBN-13: 9780470639054
• ISBN-10: 0470639059
• Artikelnr.: 34744535

Autorenporträt

MAHENDRA MALLICK, PhD, is Principal Research Scientist at the Propagation Research Associates, Inc. A senior member of the IEEE, he has served as the associate editor-in-chief of the online journal of the International Society of Information Fusion (ISIF). VIKRAM KRISHNAMURTHY, PhD, holds the Canada Research Chair in Statistical Signal Processing at The University of British Columbia. He is an IEEE Fellow and Editor-in-Chief of the IEEE Journal of Selected Topics in Signal Processing. BA-NGU VO, PhD, is Professor and Chair of Signals and Systems in the Department of Electrical and Computer Engineering at Curtin University in Western Australia. He is Associate Editor for IEEE Transactions on Aerospace and Electronic Systems.

Inhaltsangabe

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