Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains

Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains

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Most multi-target tracking filters assume that one target and its observation follow a Hidden Markov Chain (HMC) model, but the implicit independence assumption of the HMC model is invalid in many practical applications, and a Pairwise Markov Chain (PMC) model is more universally suitable than southwestern aztec rug the traditional HMC model.A set of weighted particles is used to approximate the probability hypothesis density of multi-targets in the framework of the PMC model, and a particle probability hypothesis here density filter based on the PMC model (PF-PMC-PHD) is proposed for the nonlinear multi-target tracking system.Simulation results show the effectiveness of the PF-PMC-PHD filter and that the tracking performance of the PF-PMC-PHD filter is superior to the particle PHD filter based on the HMC model in a scenario where we kept the local physical properties of nonlinear and Gaussian HMC models while relaxing their independence assumption.