Multiple-target tracking and identity management in clutter, with application to aircraft tracking

In this paper, the problem of tracking and managing the identity of multiple targets in a cluttered environment is discussed and applied to passive radar tracking of aircraft. The targets are assumed to be hybrid systems. We propose a filter based on joint probabilistic data association for target-measurement correlation combined with an identity management algorithm and an algorithm that we have developed in earlier work for hybrid state estimation. The multiple-target tracking and identity management algorithm, also incorporates suitable local information, when available, in a manner that decreases the uncertainty, as measured by system entropy. In situations in which local information is not explicitly available, a version of local information incorporation based on multiple hypothesis testing is included to improve identity management. The algorithm allows us to track multiple targets, each capable of multiple modes of operation, in the presence of interference which could be both noise in the continuous processes as well as in the form of spurious measurements.