强激光与粒子束2024，Vol.36Issue(4)：113-122,10.DOI:10.11884/HPLPB202436.230119
基于压缩残差网络的雷达辐射源识别方法研究
Radar radiation source recognition method based on compressed residual network
摘要
Abstract
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.关键词
压缩残差网络/时频分析/雷达辐射源识别/深度学习/扩张卷积Key words
compressed residual network/time-frequency analysis/radar radiation source recognition/deep learning/dilateded convolution分类
信息技术与安全科学引用本文复制引用
郭恩泽,刘正堂,崔博,刘国彬,史航宇,蒋旭..基于压缩残差网络的雷达辐射源识别方法研究[J].强激光与粒子束,2024,36(4):113-122,10.基金项目
国家自然科学基金项目(61571043) （61571043）
