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

Abstract

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 ${\text{MBM}}_{01}$ 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.

Published in Journals of Advances in Information Fusion, Special issue on Multiple Hypothesis Tracking, Volume 14, Number 2, Page 213-235, December 2019. MATLAB code is available at https://github.com/yuhsuansia/Multi-scan-trajectory-PMBM-filter

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• Trajectory Poisson multi-Bernoulli filters
• A Poisson multi-Bernoulli mixture filter for coexisting point and extended targets
• Continuous-discrete multiple target tracking with out-of-sequence measurements
• Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter
• Tracking multiple spawning targets using Poisson multi-Bernoulli mixtures on sets of tree trajectories
• Multiple Object Trajectory Estimation Using Backward Simulation