  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.
