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Contrastive Psudo-supervised Classification for Intra-Pulse Modulation of Radar Emitter Signals Using data augmentation
The automatic classification of radar waveform is a fundamental technique in electronic countermeasures (ECM). Recent supervised deep learning-based methods have achieved great success in a such classification task. However, those methods require enough labeled samples to work properly and in many circumstances, it is not available. To tackle this problem, in this paper, we propose a three-stages deep radar waveform clustering(DRSC) technique to automatically group the received signal samples without labels. Firstly, a pretext model is trained in a self-supervised way with the help of several data augmentation techniques to extract the class-dependent features. Next, the pseudo-supervised contrastive training is involved to further promote the separation between the extracted class-dependent features. And finally, the unsupervised problem is converted to a semi-supervised classification problem via pseudo label generation.
