Poisson Multi-Bernoulli Approximations for Multiple Extended Object Filtering
Yuxuan Xia, Karl Granström, Lennart Svensson, Maryam Fatemi, Ángel F. García‐Fernández, Jason Williams
Abstract
The Poisson multi-Bernoulli mixture (PMBM) is a multiobject conjugate prior for the closed-form Bayes random finite set filter. The extended object PMBM filter provides a closed-form solution for multiple extended object filtering with standard models. This article considers computationally lighter alternatives to the extended object PMBM filter by propagating a Poisson multi-Bernoulli (PMB) density through the filtering recursion. A new local hypothesis representation is presented, where each measurement creates a new Bernoulli component. This facilitates the developments of methods for efficiently approximating the PMBM posterior density after the update step as a PMB. Based on the new hypothesis representation, two approximation methods are presented: one is based on the track-oriented multi-Bernoulli (MB) approximation, and the other is based on the variational MB approximation via Kullback–Leibler divergence minimization. The performance of the proposed PMB filters with gamma Gaussian inverse-Wishart implementations are evaluated in a simulation study.