Particle Probability Hypothesis Density Filter based on Pairwise Markov Chains
Abstract
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.
Keywords
Cite
@article{arxiv.1811.12211,
title = {Particle Probability Hypothesis Density Filter based on Pairwise Markov Chains},
author = {Jiangyi Liu and Chunping Wang and Wei Wang},
journal= {arXiv preprint arXiv:1811.12211},
year = {2018}
}