The trajectory motion model based TPHD and TCPHD filters for maneuvering targets

In this article, we present the TMM-TPHD and TMM-TCPHD filters, which are the alternative trajectory probability hypothesis density (TPHD) and the alternative trajectory cardinality probability hypothesis density (TCPHD) filters for tracking maneuvering targets. To describe the trajectory maneuver, we define the motion model history sequence of a trajectory as the trajectory motion model (TMM) variable. By incorporating the TMM, we augment the general trajectory model to accommodate the maneuver. Based on this augmented model, we propose the TMM extensions of the standard TPHD and TCPHD filters, namely TMM-TPHD and TMM-TCPHD filters. These filters can directly estimate the number, trajectory state, and TMM of maneuvering targets. In addition to the derivation of the filtering formulas, we also develop linear Gaussian mixture (LGM) implementations for TMM-TPHD and TMM-TCPHD filters. We verify the tracking performance and parameter stability of the proposed filters through tracking simulations involving multiple maneuvering targets.