An explicit track continuity algorithm for the SMC-PHD filter

In multi-target tracking, the real-time performance, state-estimates accuracy, and track continuity are affected by clutter, missed detection, and closely spaced targets. To solve these problems, an improved sequential Monte Carlo implementation (SMC) of the probability hypothesis density (PHD) filter is proposed in this paper. First, based on double one-to-one principles, particle labeling approach and weight redistribution scheme for particle cloud are proposed to shield against the negative effects of clutter in high prior density region on the estimation. Second, the multi-estimate extraction are converted into multiple single-estimate extractions, which can provide the identities of the individual targets; thus, explicit track maintenance can be obtained. The results of numerical experiments demonstrate that the proposed approach can achieve explicit track continuity and better performance compared to the basic SMC-PHD filter, in terms of faster processing speed and superior estimation accuracy.