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

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.

17 pages, 11 figures, journal paper

References in corpus (2)

• Multi-Scan Implementation of the Trajectory Poisson Multi-Bernoulli Mixture Filter
• A comparison between PMBM Bayesian track initiation and labelled RFS adaptive birth

Cited by in corpus (3)

• Arithmetic Average Density Fusion -- Part II: Unified Derivation for Unlabeled and Labeled RFS Fusion
• Poisson multi-Bernoulli mixture filter with general target-generated measurements and arbitrary clutter
• Non-myopic GOSPA-driven Gaussian Bernoulli Sensor Management