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.Comment: 13 page