Bayesian Multiple Target Tracking

Two mathematicians and an engineer with a consulting firm trace their experience with tracking to the CASP search and rescue planning program they developed for the Coast Guard in the 1970s, applying a stochastic model to predict the target's position and motion over time. While most books on tracking present algorithms for problems in which contacts are received at a high data rate with good localization information about the targets, the focus here is on low data rate, low signal-to-noise ratio situations where sensor responses provide ambiguous information about the target's state. They explain Bayesian inference as a statistical decision theory framework from which to view and design tracking algorithms. Includes an appendix on Gaussian density lemma. Annotation c. Book News, Inc., Portland, OR (booknews.com)