A multi-sensor track association algorithm based on K-Medoids clustering.

To solve the problem of track association in dense target environment, a new track association algorithm based on K-Medoids clustering is presented. The algorithm makes the sensor tracks associate with the system tracks and selects the system tracks as Medoids, which greatly reduce the number of track pairs needed to associate, avoid the inherent defects of K-Medoids clustering, and improve the efficiency of the algorithm. In addition, the approximate distance between the two tracks is acquired by calculating the distance at a sampling point with the method of infinite norm, and then the track pair with minimum distance is selected as the associated tracks. The consideration of the historical and current track improves the probability of correct association. Finally, the algorithm is implemented in the multi-sensor and multi-target environment and the simulation results prove the effectiveness and superiority of the algorithm. The algorithm has a strong robustness in the presence of noise and outliers, and is suitable for the dense target environments. [ABSTRACT FROM AUTHOR]