基于SRNN+Attention+CNN的雷达辐射源信号识别方法  

Radar emitter signal recognition method based on SRNN+Attention+CNN

作　　者：高诗飏 董会旭 田润澜[2] 张歆东[1] GAO Shiyang;DONG Huixu;TIAN Runlan;ZHANG Xindong(College of Electronics Science and Engineering,Jilin University,Changchun 130012,China;School of Aviation Operations and Services,Aviation University of Air Force,Changchun 130022,China)

机构地区：[1]吉林大学电子科学与工程学院,吉林长春130012 [2]空军航空大学航空作战勤务学院,吉林长春130022

出　　处：《系统工程与电子技术》2021年第12期3502-3509,共8页Systems Engineering and Electronics

基　　金：国家自然科学基金(61571462)资助课题。

摘　　要：针对低信噪比条件下雷达辐射源信号特征提取困难、识别准确率低的问题,提出一种基于切片循环神经网络(sliced recurrent neural networks,SRNN)、注意力机制和卷积神经网络(convolutional neural networks,CNN)的雷达辐射源信号识别方法,并在CNN中引入批归一化层,进一步提升网络的识别能力。模型以雷达辐射源信号幅度序列作为输入,自动提取信号特征,输出识别结果。实验结果表明,SRNN相比于门控循环单元(gated recurrent unit,GRU)训练速度大大提升,注意力机制和批归一化层能有效提高识别准确率;在采用8种常见雷达辐射源信号进行的实验中,所提方法在低信噪比条件下仍有较高的识别准确率。Aiming at solving the problem of difficulty in extracting features of radar emitter signals and low recognition accuracy under the condition of low signal to noise ratio,a radar emitter signal based on sliced recurrent neural networks(SRNN),attention mechanism and convolutional neural networks(CNN)is proposed.Batch normalization layer is introduced into CNN to further improve the recognition ability of the network.Taking the amplitude sequence of radar emitter signal as input,the signal characteristic is extracted automatically and the recognition result of radar emitter signal is output.Compared with gated recurrent unit(GRU),the experimental results show that the training speed of SRNN is greatly improved,and the attention mechanism and batch normalization layer can effectively improve the recognition accuracy.In the experiments with eight common radar emitter signals,the proposed method still has a high recognition accuracy under the condition of low signal to noise ratio.

关 键 词：辐射源信号识别 切片循环神经网络 卷积神经网络 注意力机制 批归一化 时间序列 

分 类 号：TN971.1[电子电信—信号与信息处理]