论文标题
使用数据增强的对比性psudo监督分类雷达发射极信号的脉冲调制
Contrastive Psudo-supervised Classification for Intra-Pulse Modulation of Radar Emitter Signals Using data augmentation
论文作者
论文摘要
雷达波形的自动分类是电子对策（ECM）的基本技术。基于深度学习的深度方法在此类分类任务中取得了巨大的成功。无论如何，这些方法需要足够的标签样品才能正常工作，并且在许多情况下都无法使用此问题。在此论文中，我们可以在此论文中解决这一问题。 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.仿真结果表明，所提出的算法可以有效提取依赖类的特征，表现优于几种无监督的聚类方法，甚至可以与受监督的基于深度学习的方法达到同等的性能。
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.
