A new nearest-neighbor association approach based on fuzzy clustering

The main components of tracking in multisensor data fusion systems are estimation and data association. This paper proposes a new nearest-neighbor fuzzy association approach for multitarget tracking in a cluttered environment. In the proposed approach, fuzzy clustering is used to generate a likelihood measure in place of the traditional Mahalanobis distance. First, measurements-to-tracks associations are computed jointly across all targets and all validated measurements using fuzzy clustering, then nearest-neighbor correlation is performed based on fuzzy correlation weights. For a given target, the validated measurement that has the maximum fuzzy correlation weight is used for updating the state of the target. The proposed approach determines the association between the measurements and the tracks based on a single correlation matrix, thus it highly reduces the computational complexity compared to the joint probabilistic data association filter and the conventional fuzzy logic data association approaches. The performance of the proposed approach is evaluated using Monte Carlo simulations and compared to that of the nearest-neighbor association with Mahalanobis distance, conventional fuzzy logic data association approaches, and joint probabilistic data association filter. The results show that the proposed approach achieves better performance compared to the nearest-neighbor association with Mahalanobis distance and the conventional fuzzy logic data association approaches. The results also show that the performance of the proposed approach is not far from the performance of the joint probabilistic data association filter. Suggested steps for the proposed approach to be utilized to counter against Electronic Counter Measures techniques are also presented.