Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter

TL;DR

This paper introduces a clustering and merging method for the Poisson multi-Bernoulli mixture filter to improve computational efficiency in large-scale multiple target tracking scenarios.

Contribution

It presents a measurement-driven clustering algorithm and Bernoulli merging strategies to reduce complexity and enhance tracking performance for high target counts.

Findings

Effective reduction in computational complexity.

Successful tracking of over one thousand targets.

Improved data association through clustering.

Abstract

This paper proposes a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter to lower its computational complexity and make it suitable for multiple target tracking with a high number of targets. We define a measurement-driven clustering algorithm to reduce the data association problem into several subproblems, and we provide the derivation of the resulting clustered PMBM posterior density via Kullback-Leibler divergence minimisation. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and inter-track swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than one thousand targets.