A track correlation algorithm for multi-sensor integration

A track correlation algorithm in a multi-sensor integration system for a surveillance mission is proposed. The performance of such track correlation processing is strongly dependent not only on the target state estimation error distribution but also on the target spatial density distribution. Therefore, a track correlation problem is formulated as the likelihood ratio test problem which can take both target state estimation error distribution and target state spatial density distribution into consideration. From this formulation, the correlation algorithm for on-line processing is derived by modifications and approximations. Through analytical evaluations and simulation studies, it is shown that the proposed algorithm is superior to the conventional nearest neighbor algorithm.