Robust Poisson Multi-Bernoulli Filter With Unknown Clutter Rate
Weijian Si, Hongfan Zhu, Zhiyu Qu · IEEE Access · 2019
In Bayesian multi-target tracking (MTT), knowledge of clutter intensity is required for effective multi-target state estimation. In this paper, we propose an online multi-target filter that can operate under background with unknown clutter intensity. Our solution is based on the Poisson multi-Bernoulli mixture (PMBM) filter that jointly estimating the multi-target state and clutter rate. The unknown clutter rate is modeled as Gamma distribution, consequently, the derived PMBM recursion that adapts for unknown clutter intensity remains closed. Moreover, we adopt a Gibbs sampler to find the finite number of global hypotheses, then the multi-Bernoulli mixture is approximated by a multi-Bernoulli distribution based on a simple fusion strategy. The derived Poison multi-Bernoulli (PMB) filter has a similar form with labeled multi-Bernoulli filter (LMB) but has a straightforward prediction step. Simulations conducted for linear-Gaussian models are presented to verify that the proposed algorithm can adapt to the background with unknown clutter intensity and yield reliable tracking performance.