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

January 7th, 2020

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.

Download the full paper here.

For more information, contact us.

Subscribe to our News via Email

Enter your email address to subscribe and receive notifications of new posts by email.