A Poisson multi-Bernoulli mixture filter for coexisting point and extended targets

TL;DR

This paper introduces a Poisson multi-Bernoulli mixture filter capable of simultaneously tracking point and extended targets, with derivations for general measurement models and efficient approximations, validated through simulations.

Contribution

It develops a novel PMBM filter for coexisting point and extended targets, including a computationally efficient PMB approximation and a unified filtering recursion.

Findings

The filters effectively track mixed target types in simulations.

The PMB approximation reduces computational complexity.

The proposed methods handle general measurement models.

Abstract

This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter for coexisting point and extended targets, i.e., for scenarios where there may be simultaneous point and extended targets. The PMBM filter provides a recursion to compute the multi-target filtering posterior based on probabilistic information on data associations, and single-target predictions and updates. In this paper, we first derive the PMBM filter update for a generalised measurement model, which can include measurements originated from point and extended targets. Second, we propose a single-target space that accommodates both point and extended targets and derive the filtering recursion that propagates Gaussian densities for point targets and gamma Gaussian inverse Wishart densities for extended targets. As a computationally efficient approximation of the PMBM filter, we also develop a Poisson multi-Bernoulli (PMB) filter for coexisting point and extended targets. The resulting filters are analysed via numerical simulations.