Track-to-track association in the presence of sensor bias and the relative bias estimation

Track to track association problem is crucial for multisensor data fusion, and become complicated in the presence of sensor bias, random errors, false tracks and missed tracks. In this paper, we apply the optimal Bayes joint decision and estimation(JDE) which was proposed in [12] to the track-to-track association in the presence of sensor bias and develop a simplified JDE. The optimal Bayes JDE accounts to the possible association error when estimates the relative sensor bias and takes the estimate error into consideration when chooses the track-to-track association by the association cost having the estimation error. Hence it can enhance the accuracy of track-to-track association and reduce the relative bias estimate error. For computational simplicity, in this paper we proposed another JDE algorithm which can reduce the computation and only is slightly worse than the optimal Bayes JDE. The simulations compare the performance of the JDE association, the simplifed JDE developed in this paper, the method proposed in [8], and verify the feasibility and effectiveness of methods.