Track association with kinematic and non-kinematic state histories

Abstract

Abstract In distributed surveillance systems, a key problem is to associate track estimates that are generated by local processors to obtain a unified set of tracks. Existing track association techniques do not use track histories, and they do not take into account non-kinematic information such as target class that may be available. This paper presents a new technique that addresses these issues. We give three track association tests using histories of generalised tracks with states that can have kinematic and non-kinematic components. The track association tests are derived by minimising Bayesian risk functions where explicit recursive analytical expressions are obtained for kinematic and non-kinematic likelihood ratios. When the risk function is chosen to be the average probability of error, the test yields a maximum a posteriori (MAP) decision rule. The tests compare likelihoods of fused generalised track histories states to non-fused ones, hence they are computationally more expensive than the traditional track association tests. However, they are optimal in Bayesian sense and they do not require significant amounts of additional memory even though they use generalised track histories.