Joint multi-target filtering and track maintenance using improved labeled particle PHD filter

Abstract

As is known, the most prominent advantage of Finite Set Statistics (FISST) based multi-target tracking algorithms is it could cope with complicated tracking problems arising from special events such as target birth, target death and tracks crossing without complicated data association. Through improving the existing labeled particle Probability Hypothesis Density (L-P-PHD) filter, an improved labeled particle PHD (IL-P-PHD) filter is proposed in this paper. Simulation experiment shows that the tracking performance of IL-P-PHD filter is much better than L-P-PHD filter on complicated multi-target tracking problems, IL-P-PHD filter could extract target track information while efficiently detecting target birth and disappearance and stably estimating target state.