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

Informationen zum Autor 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 . Klappentext A unique guide to the state of the art of tracking, classification, and sensor managementThis 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. Zusammenfassung 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. Inhaltsverzeichnis 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

List of contents

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-De

Product details

• Authors: Vikram Krishnamurthy, M Mallick, Mahendra Mallick, Mahendra Krishnamurthy Mallick, Ba-Ngu Vo
• Assisted by: Vikra Krishnamurthy (Editor), Vikram Krishnamurthy (Editor), Krishnamurthy Vikram (Editor), Mahendra Mallick (Editor), Mallick Mahendra (Editor), Ba-Ngu Vo (Editor), Vo Ba-Ngu (Editor)
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 14.12.2012
• EAN: 9780470639054
• ISBN: 978-0-470-63905-4
• No. of pages: 736
• Subjects: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering; Sensor, Mustererkennung, Signalverarbeitung, Signal Processing, Electrical & Electronics Engineering, Elektrotechnik u. Elektronik, Sensors, Instrumentation & Measurement, Sensoren, Instrumente u. Messung, Pattern Analysis