Particle Probability Hypothesis Density Filter based on Pairwise Markov Chains
This work improves multi-target tracking accuracy in scenarios where target-observation independence assumptions fail, though it appears incremental as it modifies an existing filter framework.
The authors tackled multi-target tracking by proposing a particle probability hypothesis density filter based on Pairwise Markov Chains (PF-PMC-PHD) to address the invalid independence assumption in traditional Hidden Markov Chain models, showing superior tracking performance compared to the HMC-based filter in simulations.
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.