The application and improvement of gray correlation alogrithm in multisensor data association

Multisensor data association is very important in multisensor information fusion, which will decide whether the data from different sensors is representative for the same target. Tradition data association uses the joint probabilistic data association (JPDA) algorithm as the priority means, which depends on the state measurement and has long computation time. So, in this paper, a new algorithm based on gray correlation theory and information entropy theory is presented. It uses the gray correlation algorithm to get the data correlation result and uses the index entropy to get the weight factor of gray correlation, which makes the data correlation more accurate. Experiments show that the proposed algorithm is effective and accurate, which has much lower computation amount than JPDA.