A new fuzzy clustering approach for data association and track fusion in multisensor-multitarget environment

This paper develops a fuzzy clustering 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 better than the performance of the Bayesian minimum mean square error criterion.