Active jamming recognition based on bilinear EfficientNet and attention mechanism

Abstract

As electromagnetic environments are increasingly complex, there are more kinds of radar jamming signals. Active jamming recognition has problems of the low recognition accuracy and the high computational complexity, especially under a low jamming-to-noise ratio (JNR). Herein, a deep learning network based on bilinear EfficientNet and attention mechanism is proposed to recognise and classify eight kinds of jamming signals automatically. Firstly, the one-dimensional interference signal is transformed into a two-dimensional time–frequency image by the time–frequency analysis. Based on the transfer learning of EfficientNet-B3, the effective features of the time–frequency image are automatically extracted by the two-way network with attention mechanism, and the active interference classification is realised. The experimental results show that the method's overall recognition rate for eight kinds of interference signals is more than 97.5% when the JNR is −8 dB and is close to 100% at −2 dB.