Constructing membership functions using statistical data
Fuzzy Sets and Systems
Tracking and data association
Fuzzy logic for the management of uncertainty
Fuzzy logic for the management of uncertainty
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Principles of Data Fusion Automation
Principles of Data Fusion Automation
Fuzzy Control and Fuzzy Systems
Fuzzy Control and Fuzzy Systems
Mathematical Techniques in Multisensor Data Fusion
Mathematical Techniques in Multisensor Data Fusion
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Multisensor Data Fusion
IEEE Transactions on Computers
Geometrical fuzzy clustering algorithms
Fuzzy Sets and Systems
Cluster Validity for the Fuzzy c-Means Clustering Algorithrm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A parametric model for fusing heterogeneous fuzzy data
IEEE Transactions on Fuzzy Systems
Computer control of multiple site track correlation
Automatica (Journal of IFAC)
A novel conflict reassignment method based on grey relational analysis (GRA)
Pattern Recognition Letters
Information Sciences: an International Journal
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A great deal of attention is currently focused on multisensor data fusion. Multisensor data fusion combines data from multiple sensor systems to achieve improved performance and provide more inferences than could be achieved using a single sensor system. One of the most important aspects of it is track-to-track-association. This paper develops a fuzzy data fusion approach to solve the problem of track-to-track association and track fusion in distributed multisensor-multitarget multiple-attribute environments in overlapping coverage scenarios. The proposed approach uses the fuzzy clustering means algorithm to reduce the number of target tracks and associate duplicate tracks by determining the degree of membership for each target track. It uses current sensor data and the known sensor resolutions for track-to-track association, track fusion, and the selection of the most accurate sensor for tracking fused targets. Numerical results based on Monte Carlo simulations are presented. The results show that the proposed approach significantly reduces the computational complexity and achieves considerable performance improvement compared to Euclidean clustering. We also show that the performance of the proposed approach is reasonable close to the performance of the Bayesian minimum mean square error criterion.