上海交通大学学报 ›› 2022, Vol. 56 ›› Issue (7): 877-885.doi: 10.16183/j.cnki.jsjtu.2021.209

• 电子信息与电气工程 • 上一篇    下一篇

基于双通道卷积神经网络的雷达信号识别

全大英(), 陈赟, 唐泽雨, 李世通, 汪晓锋, 金小萍   

  1. 中国计量大学 信息工程学院,杭州 310018
  • 收稿日期:2021-06-15 出版日期:2022-07-28 发布日期:2022-08-16
  • 作者简介:全大英(1979-),男,浙江省丽水市人,高级工程师,从事无线测试系统设计、电子侦察信号处理和智能频谱测量计量研究. E-mail: stu_cjlu_sp@outlook.com.
  • 基金资助:
    浙江省自然科学基金项目(LQ20F020021);浙江省电磁波信息技术与计量检测重点实验室开放式项目(2019KF0003)

Radar Signal Recognition Based on Dual Channel Convolutional Neural Network

QUAN Daying(), CHEN Yun, TANG Zeyu, LI Shitong, WANG Xiaofeng, JIN Xiaoping   

  1. College of Information Engineering, China Jiliang University, Hangzhou 310018, China
  • Received:2021-06-15 Online:2022-07-28 Published:2022-08-16

摘要:

为解决在低信噪比下特征提取困难、雷达信号识别率低的问题,提出了一种基于Choi-Williams分布(CWD)和多重同步压缩变换(MSST)的双通道卷积神经网络模型.模型通过对雷达信号进行CWD和MSST时频分析,分别获取二维时频图像并进行预处理,然后送入双通道卷积神经网络进行深度特征提取,最后将两路通道获取的特征进行融合,通过卷积神经网络分类器实现对雷达信号的分类识别.仿真结果表明:在信噪比为 -10 dB时,所提模型整体识别准确率能达到96%以上,其在低信噪比下表现优异.

关键词: 低信噪比, Choi-Williams分布, 多重同步压缩变换, 双通道卷积神经网络

Abstract:

In order to solve the problems of difficult feature extraction and low recognition rate of radar signal at low signal-to-noise ratios, a dual channel convolutional neural network model based on Choi-Williams distribution (CWD) and multisynchrosqueezing transform (MSST) is proposed, which obtains two-dimensional time-frequency images by CWD and MSST time-frequency analyses on radar signals. Respectively, the time-frequency images are preprocessed and sequencely fed to a dual channel convolutional neural network for deep feature extraction. Finally, the features acquired by the two channels are fused, and the radar signal is classified and recognized through the convolutional neural network classifier. The simulation results show that when the signal-to-noise ratio is -10 dB, the overall recognition accuracy can reach above 96%, which is excellent at low signal-to-noise ratios.

Key words: low signal-to-noise ratios, Choi-Williams distribution (CWD), multisynchrosqueezing transform (MSST), dual-channel convolutional neural network

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