Abstract: A novel fast data association method is proposed, which reduces the computational load of the association probability in filtering. Firstly, the candidate measurements of each target are clustered with the aid of the modified maximum entropy fuzzy clustering. Then the joint association probability is reconstructed by utilizing the fuzzy membership degree of the target and measurement. At the same time, in order to avoid the track coalescence, the scaling factor is introduced to modify the joint association probability. Moreover, the characteristic of the discrimination factor is analyzed and the influence of the clutter density on it is also considered, which enables the algorithm to eliminate those invalidate measurements and reduce the computational load. Finally, simulation results show that the fast data association algorithm is effective, and that the performance of tracking is better than that of the existing ones.

Key words: maximum entropy fuzzy clustering, data association, joint association probability