Radar active deception jamming recognition based on Siamese squeeze wavelet attention network

Abstract Active deception jamming recognition has gained significant attention as a crucial aspect of modern electronic warfare, and a large quantity of jamming recognition methods based on either artificial or deep learning methods have been proposed to date. In actual complex battlefields, the abundant deceptive jamming signals are extremely difficult to obtain and the deceptive jamming signals leveraged by the adversarial jammers are often significantly influenced by noise. To address these challenges a Siamese squeeze wavelet attention network (SSWAN) for radar active deception jamming recognition method is proposed. By constructing a wavelet attention module (WAM), the fine‐grained time‐frequency texture features of echo and jamming signals are preferably extracted even under prohibitively strong noise scenarios. Specifically, the Siamese structure is employed as the main backbone network to measure the similarity between jamming signals and make full use of the training samples; besides, the squeeze learning module is embedded to maintain lightweight and prevent overfitting. Experimental results demonstrate that at a jamming‐to‐noise ratio (JNR) of −8 dB, the proposed method achieves recognition accuracies above 96.3% for 6‐class and multi‐class combinational active deceptive jamming types. Overall, compared to mainstream deep networks, the proposed method exhibits superior advantages in lower JNR ratio and small samples of actual battlefield scenarios.