Integrated Tracking, Classification, and Sensor Management: Theory and Applications
ISBN-13: 978-0470639054
ISBN-10: 0470639059
Edition: 1st
Wiley-IEEE Press, Hoboken, NJ, 2013
Hardcover, 736 pages
– December 3, 2012A 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:
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.
- 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)
Companion Pieces
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Consists of 17 chapters, each by experts in the particular subject area in 5 parts as follows. This leads to a somewhat disjointed approach, concentrating on algorithmic detail. A new comer to the field may need a companion piece to ease into the field. No overall introduction or road map to fusion or system architecture such as that provided by A Review on System Architectures for Sensor Fusion Applications by Elmenreich is included, which might be a good companion volume. Another companion might be Estimation with Applications to Tracking and Navigation, by Yaakov Bar-Shalom
Consists of 17 chapters, each by experts in the particular subject area in 5 parts as follows. This leads to a somewhat disjointed approach, concentrating on algorithmic detail. A new comer to the field may need a companion piece to ease into the field. No overall introduction or road map to fusion or system architecture such as that provided by A Review on System Architectures for Sensor Fusion Applications by Elmenreich is included, which might be a good companion volume. Another companion might be Estimation with Applications to Tracking and Navigation, by Yaakov Bar-Shalom
Contents
- PART I FILTERING
- 1. Angle-Only Filtering in Three Dimensions 3 Mahendra Mallick, Mark Morelande, Lyudmila Mihaylova, Sanjeev Arulampalam, and Yanjun Yan
- 2. Particle Filtering Combined with Interval Methods for Tracking Applications 43 Amadou Gning, Lyudmila Mihaylova, Fahed Abdallah, and Branko Ristic
- 3. Bayesian Multiple Target Filtering Using Random Finite Sets 75 Ba-Ngu Vo, Ba-Tuong Vo, and Daniel Clark
- 4. The Continuous Time Roots of the Interacting Multiple Model Filter 127 Henk A.P. Blom
- PART II MULTITARGET MULTISENSOR TRACKING
- 5. Multitarget Tracking Using Multiple Hypothesis Tracking 165 Mahendra Mallick, Stefano Coraluppi, and Craig Carthel
- 6. Tracking and Data Fusion for Ground Surveillance 203 Michael Mertens, Michael Feldmann, Martin Ulmke, and Wolfgang Koch
- 7. Performance Bounds for Target Tracking: Computationally Efficient Formulations and Associated Applications 255 Marcel Hernandez
- 8. Track-Before-Detect Techniques 311 Samuel J. Davey, Mark G. Rutten, and Neil J. Gordon
- 9. Advances in Data Fusion Architectures 363 Stefano Coraluppi and Craig Carthel
- 10. Intent Inference and Detection of Anomalous Trajectories: A Metalevel Tracking Approach 387 Vikram Krishnamurthy
- PART III SENSOR MANAGEMENT AND CONTROL
- 11. Radar Resource Management for Target Tracking—A Stochastic Control Approach 417 Vikram Krishnamurthy
- 12. Sensor Management for Large-Scale Multisensor-Multitarget Tracking 447 Ratnasingham Tharmarasa and Thia Kirubarajan
- PART IV ESTIMATION AND CLASSIFICATION
- 13. Efficient Inference in General Hybrid Bayesian Networks for Classification 523 Wei Sun and Kuo-Chu Chang
- 14. Evaluating Multisensor Classification Performance with Bayesian Networks 547 Eswar Sivaraman and Kuo-Chu Chang
- 15. Detection and Estimation of Radiological Sources 579 Mark Morelande and Branko Ristic
- 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
- 17. Evidential Networks for Decision Support in Surveillance Systems 661 Alessio Benavoli and Branko Ristic
Review by Fred Daum, IEEE Fellow
All engineers and researchers interested in tracking and sensor fusion should scrutinize this new book consisting of 17 chapters in 712 pages written by the world’s experts on the subject. The book is a pleasure to read, and it is a cornucopia of practical algorithms and new theory with quantitative performance comparisons. For example, chapter 8 by Neil Gordon, et al. surveys the state-of-the-art in track-before-detect algorithms, including a thorough quantitative comparison of 5 classes of algorithms (Viterbi, Baum-Welch, particle filters, maximum likelihood PDA and histogram probabilistic MHT), showing ROC curves, one-sigma errors and computational complexity for various signal-to-noise ratios and scenarios and sensors; the algorithms are thoroughly described in clear accessible prose; an extensive list of references is included. Most chapters in this book are written at a similar high level of quality and clarity and thoroughness.
