Radar Jamming Recognition via a New Siamese Network

Radar jamming signal identification at low JNR and with limited training data are both open problems. Though multiple studies were performed previously, there still existed two problems needed to be solved. On the one hand, it is difficult to obtain discriminative presentations from the radar jamming signal that are accompanied with the solid Gaussian White Noise (GWN). On the other hand, the available data for training is difficult to obyain from the actual complex noise environment. To solve these problems, we propose a novel siamese network architecture with self attention named SA-Siam for Radar jamming signal classification. Firstly, transforming the jamming signal to the time-frequency (TF) domain where can represent higher dimensional information. Then the intra-class aggregation and inter-class separability of radar jamming signals are enhanced through the siamese network which is beneficial to learn more discriminative features from TF image especially in the case of limited data. In addition, the self attention block (SA) can further capture spatial correlations of the TF image so as to improve the anti-noise performance of the siamese network. We conducted quantitative and qualitative experiments on own dataset with a set of Deep CNNs and classical siamese algorithms. The results verify that our proposed SA-Siam can fully explore the representation of noised jamming data and dramatically improve the radar jamming classification performance under limited training data.