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.;
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Ronald P.S. Mahler
Ronald P.S. Mahler is a senior staff research scientist at Lockheed Martin Advanced Technology Laboratories in Eagan, MN. He earned his Ph.D. in mathematics from Brandeis University, Waltham, MA. He is recipient of the 2005 IEEE AESS Harry Rowe Mimno Award, the 2007 IEEE AESS M. Barry Carlton Award, and the 2007 JDL-DFG Joseph Mignogna Data Fusion Award.