Paper: Multi-Scan Implementation of the Trajectory Poisson Multi-Bernoulli Mixture Filter

The Poisson multi-Bernoulli mixture (PMBM) and the multi-Bernoulli mixture (MBM) are two multi-target distributions for which closed-form filtering recursions exist. The PMBM has a Poisson birth process, whereas the MBM has a multi-Bernoulli birth process.

This paper considers a recently developed formulation of the multi-target tracking problem using a random finite set of trajectories, through which the track continuity is explicitly established.

A multi-scan trajectory PMBM filter and a multi-scan trajectory MBM filter, with the ability to correct past data association decisions to improve current decisions, are presented. In addition, a multi-scan trajectory MBM01 filter, in which the existence probabilities of all Bernoulli components are either 0 or 1, is presented.

This paper proposes an efficient implementation that performs track-oriented N-scan pruning to limit computational complexity, and uses dual decomposition to solve the involved multi-frame assignment problem.

The performance of the presented multi-target trackers, applied with an efficient fixed-lag smoothing method, are evaluated in a simulation study.

Yuxuan Xia, Karl Granstrom, Lennart Svensson, Angel Garcia Fernandez, Jason L. Williams. Multi-Scan Implementation of the Trajectory Poisson Multi-Bernoulli Mixture Filter. Journals of Advances in Information Fusion (Special issue on Multiple Hypothesis Tracking). Dec 2019.

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