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.
作者
金涛
王晓峰
田润澜
张歆东
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)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
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。
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
rapid identification of emitter signals
time series
temporal convolution network(TCN)
one dimensional convolution network(1DCNN)
parametric linear correction element
attention mechanism
