Bayesian adaptive filters for multiple maneuvering target tracking with measurements of uncertain origin

The probabilistic data association method has been successfully used for tracking targets in the presence of source uncertainty and measurement inaccuracy. Using this technique, the problem of maneuvering-target tracking is considered. A description is given of three adaptive methods which are intrinsically different for tracking single and/or multiple targets. These methods are called the adaptive control probabilistic data association filter, the adaptive joint probabilistic data association filter, and the adaptive control joint probabilistic data association filter. They estimate the state of each target in a cluttered environment for abrupt or slow changes of the target parameters