Track Coalescence and Repulsion in Multitarget Tracking: An Analysis of MHT, JPDA, and Belief Propagation Methods

TL;DR

This paper analyzes the behaviors of JPDA, MHT, and belief propagation methods in multitarget tracking, highlighting issues like track coalescence and repulsion, and demonstrating that BP-based methods mitigate these problems effectively.

Contribution

The paper provides a comparative analysis of JPDA, MHT, and belief propagation methods, revealing that BP-based MTT reduces track coalescence and avoids track repulsion.

Findings

BP-based MTT exhibits less track coalescence.

BP-based MTT shows no track repulsion.

Numerical results confirm theoretical analysis.

Abstract

Joint probabilistic data association (JPDA) filter methods and multiple hypothesis tracking (MHT) methods are widely used for multitarget tracking (MTT). However, they are known to exhibit undesirable behavior in tracking scenarios with targets in close proximity: JPDA filter methods suffer from the track coalescence effect, i.e., the estimated tracks of targets in close proximity tend to merge and can become indistinguishable, and MHT methods suffer from an opposite effect known as track repulsion. In this paper, we review the JPDA filter and MHT methods and discuss the track coalescence and track repulsion effects. We also consider a more recent methodology for MTT that is based on the belief propagation (BP) algorithm, and we argue that BP-based MTT exhibits significantly reduced track coalescence and no track repulsion. Our theoretical arguments are confirmed by numerical results.