An Efficient PHD Filter for Multi-target Tracking with Out-of-Sequence Measurement
Qi Yang, Wei Yi · 2020
In this paper, we address the update problem of the out-of-sequence measurement (OOSM) in multi-sensor multitarget tracking (MTT). Based on the probability hypothesis density (PHD) filter in the framework of random finite set (RFS), we propose an efficient algorithm for OOSM update in the MTT scenario, named as E-OOSM-PHD, which involves two stages: retrodiction and posterior update. The stage of retrodiction facilitates the incorporation of the OOSM at the appropriate time. The posterior update is to incorporate the OOSM and compensate the cardinality at the current time, so as to improve the multi-target tracking performance. The effectiveness of the proposed algorithm is demonstrated in a challenging tracking scenario via simulation results.