Track-Oriented Marginal Poisson Multi-Bernoulli Mixture Filter for Extended Target Tracking

In this paper, we derive and propose a track-oriented marginal Poisson multi-Bernoulli mixture (TO-MPMBM) filter to address the problem that the standard random finite set filters cannot build continuous trajectories for multiple extended targets. First, the Poisson point process model and the multi-Bernoulli mixture (MBM) model are used to establish the set of birth trajectories and the set of existing trajectories, respectively. Second, the proposed filter recursively propagates the marginal association distributions and the Poisson multi-Bernoulli mixture (PMBM) density over the set of alive trajectories. Finally, after pruning and merging process, the trajectories with existence probability greater than the given threshold are extracted as the estimated target trajectories. A comparison of the proposed filter with the existing trajectory filters in two classical scenarios confirms the validity and reliability of the TO-MPMBM filter.