Data-Driven Clustering and Bernoulli Merging for the Poisson Multi-Bernoulli Mixture Filter

This article 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 minimization. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and intertrack swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than 1000 targets.