Robust Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking With Heavy-Tailed Noises

Abstract

In order to improve filtering accuracy and restrain the degradation of filtering performance caused by the heavy-tailed process and measurement noises in multi-target tracking, this paper proposes a robust Student’s t mixture probability hypothesis density (PHD) filter. In the proposed method, a Student’s t mixture is implemented to the PHD filter, which recursively propagates the intensity as a mixture of Student’s t components in PHD filtering framework. Furthermore, with the advantage of a designed judging and re-weighting mechanism, an M-estimation-based dual-gating strategy is designed for the Student’s t mixture implementation to suppress the negative effect of the heavy-tailed noises. Our proposed approach not only utilizes the Student’s t distribution to match the real heavy-tailed non-Gaussian noise well but also enhances the robustness of the Student’s t mixture-based approach via the designed dual-gating strategy. The simulation results verify that the proposed algorithm can keep good filtering accuracy in the presence of the process and measurement outliers simultaneously.