This Second edition has undergone substantial revision from the 1999 first edition, recognizing that much has changed in the held of Multiple target tracking. One of the most dramatic changes is in the widespread use of particle filters to implement nonlinear, non-Gaussian Bayesian trackers. This book views multiple target tracking as a Bayesian inference problem. Within this framework it develops the theory of single target tracking, multiple target tracking, and likelihood ratio detection and tracking. In addition to providing a detailed description of a basic particle filter that implements the Bayesian single target recursion, this resource provides numerous examples of the use of particle, filters that illustrate the developed concepts, algorithms, and approaches. This book helps engineers and researchers develop tracking solutions when: Observations are nonlinear functions of the target state; Target state distributions or measurement error distributions are non-Gaussian; Data rate and signal-to-noise ratio are low; Notions of contact and data association are merged or unresolved among more than one target. By stressing both understanding and methodology, this book provides an experienced-based resource with which, engineers and researchers can design and build new solutions for novel problems.