Abstract: The radar’s tracking of a flying target is often interrupted,which seriously affects the perception of the airfield situation.Deep learning has powerful learning capabilities and has been gradually used to solve the problem of interrupted track asso-ciation.However,the existing deep learning-based interrupted track association methods fail to fully consider the similarity between the old and new track features,hence the association performance needs to be improved.Therefore,this paper proposes a track segment association algorithm based on deep temporal contrasting(TSADTC),which includes a track feature extraction mo-dule,a time comparison module,a track feature comparison module and a classifier module.The track feature extraction module uses the bidirectional LSTM(Bi-LSTM) and the encoder-decoder to extract the features of the new and the old tracks,respectively.In the time comparison module,the features of a track are used to predict the other track,so that the features of the two tracks of the same target have high similarity.The track feature comparison module calculates the feature difference of the two tracks,which is fed into the classifier to decide the association probability of the two tracks.The track pair with the largest association probability is set as the associated tracks.Experimental results show that the proposed algorithm TSADTC can effectively improve the performance of correct association rate,false association rate and missing track association rate of interrupted track association.

Key words: Temporal contrasting, Track segment association, Encoder-Decoder, Bi-LSTM