The Multiple Model Poisson Multi-Bernoulli Mixture Filter for Extended Target Tracking
Xingxiang Xie, Yang Wang, Junqi Guo, Rundong Zhou
Abstract
In an intelligent transportation system (ITS), the single motion model is not enough to meet the needs of simulation systems (or practical applications) and guarantee good tracking performance for multiple maneuvering extended target tracking (ETT). This article presents multiple models Poisson multi-Bernoulli mixture (MM-PMBM) filter for ETT to address this problem. To make multiple models’ transform in an accurate manner, the jump Markov system (JMS) is timely introduced in filtering recursion. After that, the model probability of target is recursively updated and used to estimate the target position. In addition, we derive the closed-form solution to the proposed filter based on the Gamma Gaussian inverse-Wishart (GGIW) implementation. The approach to handling the merging problem of GGIW components via Kullback–Leibler divergence (KLD) minimization is also presented, resulting in better utilization of available components. The simulation results demonstrate that the proposed filter has superior performance in comparison with several state-of-the-art multitarget filters.