The Set IMMJPDA filter for multitarget tracking

The Set JPDA (SJPDA) filter is a recently developed multi-target tracking filter that utilizes the relation between the density of a random finite set and the ordinary density of a state vector to improve on the Joint Probabilistic Data Association (JPDA) filter. One advantage to the filter is the improved accuracy of the Gaussian approximations of the JPDA, which results in avoidance of track coalescence. Another advantage is an improved estimation accuracy in terms of a measure which disregards target identity. In this paper we extend the filter to also consider multiple motion models. As a basis for the extension we use the Interacting Multiple Model (IMM) algorithm. We derive three alternative filters that we jointly refer to as Set IMMJPDA (SIMMJPDA). They are based on two alternative descriptions of the IMMJPDA filter. In the paper, we also present simulation results for a two-target tracking scenario, which show improved tracking performance for the Set IMMJPDA filter when evaluated with a measure that disregards target identity