New Accuracy Evaluation Index for Track Fusion Algorithms

Abstract

When evaluating the track fusion algorithm, common accuracy indexes may fail to evaluate the fusion accuracy correctly when the state estimation and the real target cannot be one-to-one, and fail to effectively distinguish the performance of the algorithm when the state estimation is similar. Therefore, it is necessary to construct a high-resolution evaluation index, which can evaluate the track fusion algorithm more accurately, reasonably and comprehensively. Firstly, the advantages and disadvantages of the optimal subpattern assignment (OSPA) distance as the accuracy index to evaluate the track fusion algorithm are analyzed. Then, its deficiencies are improved by using the Hellinger distance instead of the original Euclidean distance, and the distance is index transformed. Finally, a new evaluation index for track fusion algorithms is proposed, which is the OSPA distance based on Hellinger distance and index transformation. The simulation results show that the new index can not only correctly evaluate the fusion precision, but also consider the state uncertainty, making that can evaluate the track fusion algorithm more sensitively, and effectively solves the sensitivity of the index to the cut-off parameter c through index transformation.