A GGIW PMBM Smoother for Multiple Extended Object Tracking

Abstract

Poisson multi-Bernoulli mixture (PMBM) filtering is an effective method for multi-extended target tracking. However, it has a low utilization of information from multiple time scans, leading to potential errors such as jumps and discontinuities in the target trajectories. To address this, propose a gamma Gaussian inverse Wishart PMBM filter that uses multiple time scans for smoothing. The results of continuous tracking are smoothed and corrected at regular intervals, resulting in more accurate tracking performance. Simulation results show that the proposed algorithm effectively reduces generalized optimal sub-pattern assignment errors compared to the original PMBM filter.