Fundamentals of object tracking

Kalman filter, particle filter, IMM, PDA, ITS, random sets... The number of useful object-tracking methods is exploding. But how are they related? How do they help track everything from aircraft, missiles and extra-terrestrial objects to people and lymphocyte cells? How can they be adapted to novel applications? Fundamentals of Object Tracking tells you how. Starting with the generic object-tracking problem, it outlines the generic Bayesian solution. It then shows systematically how to formulate the major tracking problems - maneuvering, multiobject, clutter, out-of-sequence sensors - within this Bayesian framework and how to derive the standard tracking solutions. This structured approach makes very complex object-tracking algorithms accessible to the growing number of users working on real-world tracking problems and supports them in designing their own tracking filters under their unique application constraints. The book concludes with a chapter on issues critical to successful implementation of tracking algorithms, such as track initialization and merging ''Kalman filter, particle filter, IMM, PDA, ITS, random sets . . . The number of useful object tracking methods is exploding. But how are they related? How do they help to track everything from aircraft, missiles and extra-terrestrial objects to people and lymphocyte cells? How can they be adapted to novel applications? Fundamentals of Object Tracking tells you how. Starting with the generic object tracking problem, it outlines the generic Bayesian solution. It then shows systematically how to formulate the major tracking problems - maneuvering, multi-object, clutter, out-of-sequence sensors - within this Bayesian framework and how to derive the standard tracking solutions. This structured approach makes very complex object tracking algorithms accessible to the growing number of users working on real-world tracking problems and supports them in designing their own tracking filters under their unique application constraints. The book concludes with a chapter on issues critical to the successful implementation of tracking algorithms, such as track initialization and merging''-- Read more... Machine generated contents note: Preface; 1. Introduction to object tracking; 2. Filtering theory and non-manoeuvring object tracking; 3. Manoeuvring object tracking; 4. Single object tracking in clutter; 5. Single and multiple object tracking in clutter: existence based approach; 6. Multiple object tracking in clutter: random set based approach; 7. Bayesian smoothing algorithms for object tracking; 8. Object tracking with time delayed, out-of-sequence measurements; 9. Practical object tracking; A. Mathematical and statistical preliminaries; B. Finite set statistics; C. Pseudo functions in object tracking; Bibliography; Index "Kalman filter, particle filter, IMM, PDA, ITS, random sets ... The number of useful object tracking methods is exploding. But how are they related? How do they help to track everything from aircraft, missiles and extra-terrestrial objects to people and lymphocyte cells? How can they be adapted to novel applications? Fundamentals of Object Tracking tells you how. Starting with the generic object tracking problem, it outlines the generic Bayesian solution. It then shows systematically how to formulate the major tracking problems - maneuvering, multi-object, clutter, out-of-sequence sensors - within this Bayesian framework and how to derive the standard tracking solutions. This structured approach makes very complex object tracking algorithms accessible to the growing number of users working on real-world tracking problems and supports them in designing their own tracking filters under their unique application constraints. The book concludes with a chapter on issues critical to the successful implementation of tracking algorithms, such as track initialization and merging"-- Provided by publisher Frontmatter 1 Contents 5 Preface 9 1 - Introduction to object tracking 13 2 - Filtering theory and non-maneuvering object tracking 34 3 - Maneuvering object tracking 74 4 - Single-object tracking in clutter 115 5 - Single- and multiple-object tracking in clutter: object-existence-based approach 145 6 - Multiple-object tracking in clutter: random-set-based approach 235 7 - Bayesian smoothing algorithms for object tracking 277 8 - Object tracking with time-delayed, out-of-sequence measurements 301 9 - Practical object tracking 324 Appendix A - Mathematical and statistical preliminaries 356 Appendix B - Finite set statistics (FISST) 366 Appendix C - Pseudo-functions in object tracking 370 References 373 Index 382 Introduces object tracking algorithms from a unified, recursive Bayesian perspective, along with performance bounds and illustrative examples.