  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.
