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This book focuses on target tracking and information fusion with random finite sets. Both principles and implementations have been addressed, with more weight placed on engineering implementations. This is achieved by providing in-depth study on a number of major topics such as the probability hypothesis density (PHD), cardinalized PHD, multi-Bernoulli (MB), labeled MB (LMB), d-generalized LMB (d-GLMB), marginalized d-GLMB, together with their Gaussian mixture and sequential Monte Carlo implementations. Five extended applications are covered, which are maneuvering target tracking, target tracking for Doppler radars, track-before-detect for dim targets, target tracking with non-standard measurements, and target tracking with multiple distributed sensors. The comprehensive and systematic summarization in target tracking with RFSs is one of the major features of the book, which is particularly suited for readers who are interested to learn solutions in target tracking with RFSs. The book benefits researchers, engineers, and graduate students in the fields of random finite sets, target tracking, sensor fusion/data fusion/information fusion, etc.
List of contents
Introduction.- Single Target Tracking Algorithms.- Foundation of the Random Finite Set.- The Probability Hypothesis Density Filter.- The Cardinalized Probability Hypothesis Density Filter.- The Multi-Bernoulli Filter.- Labeled RFS-Based Filters.- Maneuvering Target Tracking.- Target Tracking for Doppler Radars.- Track-Before-Detect for Dim Targets.- Target Tracking With Non-Standard Measurements.- Target Tracking for Multiple Distributed Sensors.- Appendix.
