To Coalesce or to Repel? An Analysis of MHT, JPDA, and Belief Propagation Multitarget Tracking Methods

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.

Track Coalescence and Repulsion: MHT, JPDA, and BP

Joint probabilistic data association (JPDA) and multiple hypothesis tracking (MHT) introduced in the 70s, are still widely used methods for multitarget tracking (MTT). Extensive studies over the last few decades have revealed undesirable behavior of JPDA and MHT type methods in tracking scenarios with targets in close proximity. In particular, JPDA suffers from the track coalescence effect, i.e., estimated tracks of targets in close proximity tend to merge and can become indistinguishable. Interestingly, in MHT, an opposite effect to track coalescence called track repulsion can be observed. In this paper, we review the estimation strategies of the MHT, JPDA, and the recently introduced belief propagation (BP) framework for MTT and we investigate if BP also suffers from these two detrimental effects. Our numerical results indicate that BP-based MTT can mostly avoid both track repulsion and coalescence.