Bayesian Multiple Target Tracking

摘要: From the Publisher: Get solutions to your most challenging tracking problems with this up-to-date resource. Using Bayesian inference framework, book helps you design and develop mathematically sound algorithms for dealing involving multiple targets, sensors, platforms. The shows how non-linear Multiple Hypothesis Tracking Theory of Unified are successful methods when target must be performed without contacts or association. With detailed examples illustrating developed concepts, algorithms, approaches — you: >Track observations functions site, state distributions measurement error not Gaussian, in low data rate signal noise ratio situations, notions contact association merged unresolved among more than one target >Detect track a single sensor response is strong enough call contact >Determine bounds on tracker performance from characteristics targets sensors >Set optimal threshold levels calling likelihood detection tracking, compute probabilities joint non-geometric information