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

TL;DR

This paper introduces multi-scan trajectory PMBM and MBM filters with improved data association and computational efficiency for multi-target tracking, evaluated through simulation with fixed-lag smoothing.

Contribution

It develops multi-scan trajectory PMBM and MBM filters with track-oriented pruning and dual decomposition, enhancing multi-target tracking performance and computational efficiency.

Findings

Effective multi-target tracking demonstrated in simulations.

Improved data association accuracy with multi-scan approach.

Enhanced computational efficiency through pruning and dual decomposition.

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.