Tracking a coordinated group using expectation maximisation

Abstract

A common assumption in multiple target tracking is each target moves independently of other targets; however, there are cases where targets travel in a group. In these cases, there are dependencies between the targets; consequently, group target tracking should be used. We investigate efficient approximate methods to produce real-time group target tracking algorithms. Specifically, an algorithm based on expectation maximisation (EM) and belief propagation (BP) is introduced to track a single group of targets where the number of targets is known. The group expectation maximisation belief propagation (GEMBP) filter is compared to the joint probabilistic data association (JPDA) filter, the probabilistic multiple hypothesis tracker (PMHT) and the group PMHT for scenarios involving missed detections and clutter. We introduce a variant of the optimal subpattern assignment (OSPA) metric that includes a penalty for track swaps; results show that the GEMBP filter exhibits fewer track swaps than the JPDA filter.