Extension-Difference-Mapping-Based PMBM Filter for Non-Ellipsoidal Extended Target Tracking

Extended target tracking requires both accurate shape representation and efficient recursive estimation. In non-ellipsoidal extended target tracking, ellipsoidal random-matrix models are computationally efficient and suitable for Bayesian recursion, but they mainly describe the overall spatial dispersion of measurements and cannot represent local contour variations such as protrusions and concavities. In contrast, non-ellipsoidal contour models provide stronger shape representation but usually introduce higher computational complexity and stronger prior assumptions. To address this trade-off, this paper proposes an extension-difference-mapping-based Poisson multi-Bernoulli mixture filter, termed EDM-PMBM, for non-ellipsoidal extended target tracking. First, each local Bernoulli component carries a Fourier-based contour estimate and an ellipsoidal baseline propagated from the previous posterior. At the current scan, the predicted EDM function is used to map each candidate measurement subset into the EDM domain, where the EDM-induced GGIW likelihood is evaluated for PMBM data association. After the association is determined, the assigned measurement subset is used to update the posterior contour, the EDM ratio, and the EDM-domain state. The updated EDM information is then propagated to subsequent scans. In this way, shape differences are introduced into likelihood evaluation and data association without changing the basic recursive structure of the PMBM filter. Simulation results in two scenarios show that the proposed EDM-PMBM filter achieves lower GOSPA error than the compared filters and maintains more stable tracks in dense crossing situations. These results indicate that the proposed method improves the discrimination ability for non-ellipsoidal extended targets.