Fundamentals of Object Tracking (Repost)

Features A unified Bayesian approach to object tracking, which makes the techniques more accessible to non-specialists Presents the performance bounds of algorithms for each of the real-world scenarios studied in the book Includes illustrative examples to encourage rapid adoption and adaptation of object tracking techniques 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. Table of Contents Preface 1. Introduction to object tracking 2. Filtering theory and non-maneuvering object tracking 3. Maneuvering object tracking 4. Single-object tracking in clutter 5. Single- and multiple-object tracking in clutter: object-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 (FISST) C. Pseudo-functions in object tracking References Index.