This comprehensive resource provides you with an in-depth understanding of finite-set statistics (FISST) - a recently developed method which unifies much of information fusion under a single probabilistic, in fact Bayesian, paradigm. The book helps you master FISST concepts, techniques, and algorithms, so you can use FISST to address real-world challenges in the field. You learn how to model, fuse, and process highly disparate information sources, and detect and track non-cooperative individual/platform groups and conventional non-cooperative targets. You find a rigorous Bayesian unification for many aspects of expert systems theory. Moreover, the book presents systematic integral and differential calculus for multisource-multitarget problems, providing a methodology for devising rigorous new techniques. This accessible and detailed book is supported with over 3,000 equations, 90 clear examples, 70 explanatory figures, and 60 exercises with solutions.
Unified Single-Target Multisource Integration - Conventional Single-Sensor, Single-Target Tracking. General Data Modeling. Random Set Uncertainty Representations. Unambiguously Generated Ambiguous (UGA) Measurements. Ambiguously Generated Ambiguous (AGA) Measurements. Ambiguously Generated Unambiguous (AGU) Measurements. Ambiguous State-Estimates. Finite-Set Measurements. Unified Multitarget Multisource Integration - Conventional Multisource-Multitarget Information Fusion. Multitarget Differential and Integral Calculus. Multitarget Likelihood Functions. Multitarget Markov Densities. The Multisource-Multitarget Bayes Filter. Approximate Multitarget Filtering - Multitarget Particle Approximation. Multitarget-Moment Approximation. Multitarget Multi-Bernoulli Approximation. Appendices.