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In order to improve the accuracy and rapidity of multi-target tracking in clutter environment, the fuzzy clustering algorithm is used to obtain the relative degrees of different measurement target memberships, and the fuzzy correlation probabilities of measurement values and targets are calculated in combination with public measurement influence factors.  Then using fuzzy association information combined with Kalman filtering, each target is separately tracked through the weighted fusion method to achieve the state update of the target. The simulation results show that the combination of fuzzy clustering and joint probability data improves the fastness of the target clustering in the relatively dense environment of clutter, and the accuracy of the target tracking is ensured by the combination of Kalman filter and the weighting of the influence factor.

Dense clutter multi-target tracking algorithm based on improved fuzzy clustering data association