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

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, while MHT methods suffer from an opposite effect known as track repulsion, i.e., the estimated tracks of targets in close proximity tend to repel each other in the sense that their separation is larger than the actual distance between the targets. 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. We argue that BP-based MTT does not exhibit track repulsion because it is not based on maximum a posteriori estimation, and that it exhibits significantly reduced track coalescence because certain properties of the BP messages related to data association encourage separation of target state estimates. Our theoretical arguments are confirmed by numerical results for four representative simulation scenarios.

Keywords

• Multitarget tracking
• joint probabilistic data association
• JPDA
• multiple hypothesis tracking
• MHT
• belief propagation