The multitarget Set JPDA filter with target identity

Abstract

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 result in avoidance of track coalescence. In the original presentation of the SJPDA filter, the focus was on problems where target identity is not relevant, and it was shown that the filter performs better than the JPDA filter for such problems. The improved performance of the SJPDA is due to its relaxation of the labeling constraint that hampers most tracking approaches. However, if track identity is of interest a record of it may be kept even with a label-free approach such as the SJPDA: label-free targets are localized via the SJPDA, and then the identities are recalled as an overlay.