杂波环境下基于最大熵模糊聚类的 JPDA 算法

Aiming at the problems of low tracking accuracy and poor real-time performance of multi-target tracking data association in clutter environment, this paper proposes a joint probabilistic data association algorithm based on maximum entropy fuzzy clustering (MEFC-JPDA). Firstly, the membership obtained by the maximum entropy fuzzy clustering is used to preliminarily characterize the correlation probability between the target and the effective measurement. Secondly, the measurement correction factor based on target distance is used to adjust the correlation probability, and the correlation probability matrix is established. Finally, combined with the Kalman filtering algorithm, the state of the target is weighted updated. Simulation results show that the tracking performance of the proposed algorithm in clutter environment is greatly improved compared with the existing two association algorithms, and it is an effective multi-target tracking data association algorithm.