  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 HMC model is invalid in many practical applications, and a
Pairwise Markov Chain (PMC) model is more universally suitable than traditional
HMC model. A particle probability hypothesis density filter based on PMC model
(PF-PMC-PHD) is proposed for the nonlinear multi-target tracking system.
Simulation results show the effectiveness of PF-PMC-PHD filter, and that the
tracking performance of PF-PMC-PHD filter is superior to the particle PHD
filter based on HMC model in a scenario where we kept the local physical
properties of nonlinear and Gaussian HMC models while relaxing their
independence assumption.
