Abstract:The increasingly complex electromagnetic environment imposes high requirements for battlefield target detection. Accurate, quick and complete multi-radar track correlation has become an urgent problem with the continuous development of multi-radar fusion systems. Most of the existing research on track correlation only considers the latest target track points reported by radar, while ignoring the previous track information. In addition, the solution to the asynchronous track problem of most track correlation algorithms is time registration. It not only increases the computational cost of the algorithm, but also magnifies the error contained in the track information. Therefore, time registration is difficult to be applied to the current complex electromagnetic environment. In this paper, a Track-to-Track Correlation algorithm based on Siamese Network(TTCSN) is proposed, which is suitable for asynchronous track correlation and does not need time registration. A pair of tracks are sent into the feature extraction network, and TTCSN learns the hidden features of input tracks. Then the similarity of hidden feature vectors are calculated by TTCSN to get the similarity vector which is fed into the classifier to distinguish that the input tracks are correlated or not. The experimental results show that TTCSN algorithm can effectively solve the problem of asynchronous track correlation.