  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. The simulation results show that the proposed
algorithm can effectively extract class-dependent features, outperforming
several unsupervised clustering methods, even reaching performance on par with
the supervised deep learning-based methods.
