基于压缩残差网络的雷达辐射源识别方法研究OA北大核心CSTPCD

Aiming at the problems of low recognition accuracy and poor timeliness of existing radar emitter signal recognition methods under the condition of low SNR,this paper proposes a radar emitter signal recognition method based on compressed residual network.Using Choi-Williams distribution for reference,the time-domain signal is converted into a two-dimensional time-frequency image,which improves the effectiveness of signal essential feature extraction.According to the characteristics of the application scenario,it selects the"compression"range of convolutional neural networks(CNN),and builds a compression residual network to automatically extract image features and identify.The simulation results show that compared with other advanced models,the proposed method can reduce the running time of signal recognition by about 88%,and the average recognition rate of 14 radar emitter signals is at least 5%higher when the signal-to-noise ratio is-14 dB.This paper provides an efficient intelligent recognition method of radar emitter signal,which has potential engineering application prospects.