Characterization and detection of noise in clustering
Pattern Recognition Letters
Fuzzy logic approach to multisensor data association
Mathematics and Computers in Simulation
Hybrid fuzzy probabilistic data association filter and joint probabilistic data association filter
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent information systems and applications
Radar tracking for air surveillance in a stressful environment using a fuzzy-gain filter
IEEE Transactions on Fuzzy Systems
Online data-driven fuzzy clustering with applications to real-time robotic tracking
IEEE Transactions on Fuzzy Systems
Sensor Fusion for SLAM Based on Information Theory
Journal of Intelligent and Robotic Systems
Multitarget bearings-only tracking using fuzzy clustering technique and Gaussian particle filter
The Journal of Supercomputing
Relative entropy fuzzy c-means clustering
Information Sciences: an International Journal
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The problem of data association for target tracking in a cluttered environment is discussed. In order to deal with the problem of data association for real time target tracking, a novel data association method based on maximum entropy fuzzy clustering is proposed. Firstly, the candidate measurements of each target are clustered with the aid of the modified maximum entropy fuzzy clustering. Then the joint association probabilities are reconstructed by utilizing the fuzzy membership degree of the measurement belonging to the target. At the same time, in order to deal with the uncertainty of the measurements, a new weight assignment is introduced. Moreover, the characteristic of the discrimination factor is analyzed, and the influence of the clutter density on it is also considered, which enables the algorithm eliminate those invalidate measurements and reduce the computational load. Finally, the simulation results show that the proposed algorithms have advantages over the existing ones in terms of efficiency and low computational load.