Abstract:

Aiming at the problem of evaluation of the accuracy of track fusion algorithms, a new evaluation index is proposed. The index is based on the cardinalized optimal linear assignment metric. The Hellinger distance is used to replace the Euclidean distance of the distance error part of the metric, so that the error covariance of the state is considered and the cutoff distance is removed, making the cutoff parameter only play the role of adjusting the weight between the distance error and the cardinal error. A variable is added in the potential error part, and can be set according to the need so as to achieve the function of whether the index considers the cardinal error. Simulation experiments show that the new index can not only correctly evaluate the fusion precision, but also evaluate the pros and cons of the fusion algorithm more sensitively by considering the state uncertainty and cardinal error, and that it can effectively evaluate the fusion precision when the fusion error expectation is unknown.

Key words: track fusion, evaluation index, the cardinalized optimal linear assignment metric, Hellinger distance