系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (2): 463-469.doi: 10.12305/j.issn.1001-506X.2022.02.14

• 传感器与信号处理 • 上一篇    下一篇

基于改进1DCNN+TCN的雷达辐射源快速识别方法

金涛1, 王晓峰2, 田润澜2, 张歆东1,*   

  1. 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
  • 收稿日期:2020-12-27 出版日期:2022-02-18 发布日期:2022-02-24
  • 通讯作者: 张歆东
  • 作者简介:金涛(1997—), 男, 硕士研究生, 主要研究方向为深度学习、雷达信号处理|王晓峰(1987—), 男, 讲师, 博士, 主要研究方向为信号与信息处理|田润澜(1973—), 女, 教授, 硕士研究生导师, 博士, 主要研究方向为航空电子侦察情报分析|张歆东(1970—), 女, 教授, 博士研究生导师, 博士, 主要研究方向为信号检测、有机光电材料器件研究
  • 基金资助:
    国家自然科学基金(61571462)

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

Tao JIN1, Xiaofeng WANG2, Runlan TIAN2, Xindong ZHANG1,*   

  1. 1. College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
    2. School of Aviation Operations and Services, Aviation University of Air Force, Changchun 130022, China
  • Received:2020-12-27 Online:2022-02-18 Published:2022-02-24
  • Contact: Xindong ZHANG

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。

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

Abstract:

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.

Key words: rapid identification of emitter signals, time series, temporal convolution network (TCN), one dimensional convolution network (1DCNN), parametric linear correction element, attention mechanism

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