Tracking and data association
Characterization and detection of noise in clustering
Pattern Recognition Letters
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Robust shape detection using fuzzy clustering: practical applications
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Maximum entropy fuzzy clustering with application to real-time target tracking
Signal Processing - Special section: Distributed source coding
A study on two measurements-to-tracks data assignment algorithms
Information Sciences: an International Journal
Multitarget bearings-only tracking using fuzzy clustering technique and Gaussian particle filter
The Journal of Supercomputing
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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.