Poisson multi-Bernoulli mixture filter with general target-generated measurements and arbitrary clutter

TL;DR

This paper demonstrates that the Poisson multi-Bernoulli mixture (PMBM) density acts as a conjugate prior for general measurement and clutter models, leading to new filtering algorithms that handle complex clutter scenarios effectively.

Contribution

It establishes the PMBM density as a conjugate prior for general measurement and clutter models and derives corresponding filtering recursions, including implementations for point and extended targets.

Findings

Effective handling of non-standard clutter in simulations

Implementation of Gibbs sampling for data association

Improved multi-target tracking performance

Abstract

This paper shows that the Poisson multi-Bernoulli mixture (PMBM) density is a multi-target conjugate prior for general target-generated measurement distributions and arbitrary clutter distributions. That is, for this multi-target measurement model and the standard multi-target dynamic model with Poisson birth model, the predicted and filtering densities are PMBMs. We derive the corresponding PMBM filtering recursion. Based on this result, we implement a PMBM filter for point-target measurement models and negative binomial clutter density in which data association hypotheses with high weights are chosen via Gibbs sampling. We also implement an extended target PMBM filter with clutter that is the union of Poisson-distributed clutter and a finite number of independent clutter sources. Simulation results show the benefits of the proposed filters to deal with non-standard clutter.