空间电子技术2023，Vol.20Issue(6)：125-130,6.DOI:10.3969/j.issn.1674-7135.2023.06.017
基于卷积神经网络的雷达辐射源稳健识别方法
A robust radar radiation source recognition algorithm based on convolutional neural network
摘要
Abstract
Aiming at the problem that the radar signal recognition method based on traditional statistical features has low recognition performance and high processing complexity for complex modulation signal types under low signal-to-noise ratio,this paper proposes a robust radar emitter signal recognition method based on deep neural network.This method obtains the representation of the signal in the transform domain by extracting the instantaneous phase characteristics of the signal,and uses it as the input of the deep neural network to realize the rapid recognition of the radar emitter signal.In view of the fact that the instantaneous phase feature is sensitive to the signal-to-noise ratio,the principal component analysis method is used to denoise the signal feature domain to improve the robustness of the noise model.Through simulation experiments,the recognition performance of the proposed method for seven modulation signal types under different signal-to-noise ratios is verified.Through theoretical analysis and experimental comparison of different methods,and it verifies that the proposed algorithm has the advantages of short time-consuming,high recognition accuracy and better noise robustness,which can be applied in engineering practicability.关键词
主成分分析/雷达辐射源信号识别/卷积神经网络/相位特征Key words
principal component analysis/radar radiation source signal recognition/convolutional neural network/phase feature分类
信息技术与安全科学引用本文复制引用
郭林昱,杨新权,匡银,文伟..基于卷积神经网络的雷达辐射源稳健识别方法[J].空间电子技术,2023,20(6):125-130,6.基金项目
国家重点实验室基金(编号:2021-WDKY-SYS-DN-11) （编号:2021-WDKY-SYS-DN-11）
