Radar emitter signal recognition based on coordinated attention

Aiming at the problem that complex radar emitter signals are difficult to be recognized at low signal-to-noise ratio, a method based on improved coordinate attention network is proposed. Firstly, the radar signal is converted into a two-dimensional time-frequency image to reflect the signal feature information. Then the time-frequency image preprocessing and denoising by convolutional neural network. Finally, the coordinated attention network is used for feature extraction, and then the classification of radar emitter source signals are realized. Experiments results show that the proposed method can validly improve the accuracy of radar signal recognition under the condition of low SNR.