基于改进1DCNN+TCN的雷达辐射源快速识别方法  

Rapid recognition method of radar emitter based on improved 1DCNN+TCN

作　　者：金涛 王晓峰[2] 田润澜[2] 张歆东[1] JIN Tao;WANG Xiaofeng;TIAN Runlan;ZHANG Xindong(College of Electronic 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》2022年第2期463-469,共7页Systems Engineering and Electronics

基　　金：国家自然科学基金(61571462)资助课题。

摘　　要：为了解决传统雷达辐射源识别方式识别速度慢、在低信噪比时很难准确识别等问题,结合深度学习提出了一种基于改进一维卷积神经网络(one-dimensional convolutional neural network,1DCNN)和时间卷积网络(temporal convolutional network,TCN)的雷达辐射源快速识别模型。在1DCNN的基础上加入了批归一化层,并在全连接层前加入注意力机制;同时在原有TCN的基础上进行改进,使用Leaky ReLU激活函数代替ReLU函数;将改进后的TCN与1DCNN相连接。仿真实验结果分析表明,该模型不仅能够迅速识别出辐射源信号,识别准确率也较高,能够有效平衡模型识别速度和识别精度。In order to solve the problems of low recognition speed and that it is difficult to accurately identify radar emitter in low signal-to-noise ratios(SNRs),a fast radar emitter recognition model based on improved one-dimensional convolution neural network(1DCNN)and temporal convolution network(TCN)is proposed.In this paper,a batch normalization layer is added to the 1DCNN,and the attention mechanism is added before the full connection layer;at the same time,it is improved on the basis of the original TCN,using the Leaky ReLU activation function to replace the ReLU function;and the improved TCN is connected with 1DCNN.Through the analysis of simulation results,the model can not only identify emitter signals quickly,but also have a high accuracy rate of identification,which can effectively balance the recognition speed and model recognition accuracy.

关 键 词：辐射源信号快速识别 时间序列 时间卷积网络 一维卷积神经网络 参数化线性修正单元 注意力机制 

分 类 号：TN971.1[电子电信—信号与信息处理]