Track-to-Track Association Algorithm Based on Adaptive Clustering Threshold

In the multitarget information fusion process, the performance of the track-to-track association algorithm affect the subsequent track fusion effect directly. The traditional clustering method sets the parameter of fuzzy membership function and clustering threshold manually, which causes low selection adaptability. In this paper, we propose a track-to-track association algorithm using an adaptive clustering threshold which is based on the parameter estimation of the fuzzy membership function. The algorithm is tailored for distributed multisensor-multitarget environment. Firstly, the fuzzy membership function is constructed according to the parameter estimation. Then, the fuzzy membership function is applied for calculating the cluster threshold. Finally, the tracks for the same target are selected by soft clustering method. The effectiveness and superiority of the proposed algorithm are verified by simulation data.