Hybrid fuzzy probabilistic data association filter and joint probabilistic data association filter
Multitarget tracking problems are theoretically interesting because, unlike other estimation problems, the origins of the measurements are not identified. This involves hypothesis generation and their evaluation in terms of degree of agreement between the given measurements and the underlying tracks. Typical algorithms to deal with such problems are the probabilistic data association filter (PDAF) in the case of single target tracking and joint probabilistic data association filter (JPDAF) in the case of multiple target tracking proposed by Bar-Shalom and his team. The basis of JPDAF is the calculus of the joint probabilities over all targets and hits. The algorithm assigns weights for reasonable hits and uses a weighted centroid of those hits to update the track. In this paper, we propose a new weight assignment based on fuzzy c-means methodology. Particularly, in order to take account for the false alarms (clutter) where none of the measurements is target originated, a new noisy fuzzy c-means algorithm is elaborated. The latter contrasts with that provided by Dave regarding the location of the noise prototype as well as the meaning of the universality of the noise class. The treatment of conflictual situations where, for instance, more than one hit fail in a target extension gate is accomplished using some weighted based procedure with respect to all feasible joint matrices involved in the construction of joint probabilities in JPDAF. In the meantime, the general methodology of PDAF and JPDAF remains unchanged. This leads to Hybrid Fuzzy PDAF in the case of single target tracking and Hybrid Fuzzy JPDAF in the case of multiple target tracking. This investigation shows a fruitful combination between fuzzy and probabilistic approaches in order to accomplish target tracking tasks.
joint probabilistic data association filter target tracking fuzzy c-means