In this paper we focus on targets which, in addition to reflecting signals themselves, also have a trailing path behind them, called a wake, which causes additional detections. When the detections are fed to a tracking system like the probabilistic data association filter (PDAF), the estimated track can be misled and sometimes lose the real target because of the wake. This problem becomes even more severe in multitarget environments where targets are operating close to each other in the presence of wakes. To prevent this, we have developed a probabilistic model of the wakes in a multitarget environment. This model is used to augment the joint probabilistic data association filter (JPDAF) for both coupled and decoupled filtering.

This paper provides a systematic comparison of the standard data association filters (PDAF and JPDAF) and their modified versions presented here in a multitarget multisensor environment. Simulations of two targets with wakes in four different scenarios show that this modification gives good results and the probability of lost tracks is significantly reduced. The targets are observed by two sensors and it is shown that tracks estimated in a centralized fusion configuration are better than those from the local sensors. It is also shown that applying the wake model to targets that do not generate a wake, yields almost no deterioration of the tracking performance.