A Partitioned Poisson Multi-Bernoulli Filter

Abstract

The Poisson multi-Bernoulli (PMB) filter offers an elegant Bayesian formulation for multi-target tracking by approximating the complex PMBM density as a PMB density. This approximation allows for more efficient computation and simpler implementation while maintaining reasonable accuracy. However, approximating the posterior density involves a certain degree of tracking accuracy loss, and different approximation methods may result in different types of tracking accuracy losses. To address this problem, this paper proposes a partitioned PMB (PPMB) filter that decomposes the multi-target tracking problem to smaller sub-problems, which can synthesize the advantages of different approximations to avoid tracking accuracy loss. Furthermore, to address the issue of tracking lost for close-spaced and undetected targets, an improved measurement oriented multi-Bernoulli Poisson (IMOMB/P) filter is also proposed, which utilizes more accurate marginal association probabilities. Simulation results demonstrate that the proposed PPMB filter outperforms existing PMB filters, such as the MOMB/P and track oriented multi-Bernoulli (TOMB) filters, thus showing promising application prospects.