Contrastive time–frequency learning for radar signal sorting

Siya Mi, Hao Cheng, Yu Zhang

Abstract: With the development of radar technology, the electromagnetic environment becomes more and more complex. The acquisition of labeled data is costly, and the existing radar signal sorting and recognition methods have poor generalization capability. To address this problem, a radar signal recognition technique based on time–frequency analysis and contrastive learning is proposed in this paper. The classification process is improved according to the characteristics of radar signals without using labels. The proposed method, contrastive time–frequency learning, can use the unsupervised data to sort and identify radar signals. By augmenting the data with various channel conditions, the generalization ability of the classification model is also improved, especially for the situation with low SNR. The experiments prove the good performance of the proposed method, and classification accuracy for radar signals can be up to 98.17%.