基于时频图像和高次频谱特征的雷达信号识别

李世通; 全大英; 唐泽雨; 陈赟; 汪晓峰; 金小萍

doi:10.11959/j.issn.1000-0801.2022024

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基于时频图像和高次频谱特征的雷达信号识别

研究与开发 | 更新时间：2024-06-05

- 基于时频图像和高次频谱特征的雷达信号识别
- Time-frequency image and high-order spectrum characteristics based radar signal recognition
- 电信科学 2022年38卷第2期页码：84-91
- 作者机构：
- 作者简介：
  
  [ "李世通（1996- ），男，中国计量大学信息工程学院硕士生，主要研究方向为电子侦察信号处理" ]
  [ "全大英（1979- ），男，中国计量大学副教授、高级工程师，主要研究方向为无线测试系统设计、电子侦察信号处理和智能频谱测量计量" ]
  [ "唐泽雨（1996- ），男，中国计量大学信息工程学院硕士生，主要研究方向为电子侦察信号处理" ]
  [ "陈赟（1997- ），男，中国计量大学信息工程学院硕士生，主要研究方向为电子侦察信号处理" ]
  [ "汪晓锋（1984- ），男，博士，中国计量大学信息工程学院讲师，主要研究方向为复杂数据分析、机器学习及图神经网络" ]
  [ "金小萍（1978- ），女，中国计量大学信息工程学院副教授、硕士生导师，主要研究方向为5G通信、通信检测、物联网通信" ]
- 基金信息：
  
  浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(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;Open Project Funding of the Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province(2019KF0003)
- DOI：10.11959/j.issn.1000-0801.2022024
  中图分类号： TP393
- 网络出版日期：2022-02，
  
  纸质出版日期：2022-02-20
- 稿件说明：
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李世通, 全大英, 唐泽雨, 等. 基于时频图像和高次频谱特征的雷达信号识别[J]. 电信科学, 2022,38(2):84-91.

Shitong LI, Daying QUAN, Zeyu TANG, et al. Time-frequency image and high-order spectrum characteristics based radar signal recognition[J]. Telecommunications science, 2022, 38(2): 84-91.
李世通, 全大英, 唐泽雨, 等. 基于时频图像和高次频谱特征的雷达信号识别[J]. 电信科学, 2022,38(2):84-91. DOI： 10.11959/j.issn.1000-0801.2022024.

Shitong LI, Daying QUAN, Zeyu TANG, et al. Time-frequency image and high-order spectrum characteristics based radar signal recognition[J]. Telecommunications science, 2022, 38(2): 84-91. DOI： 10.11959/j.issn.1000-0801.2022024.

摘要

针对低信噪比下雷达信号识别准确率较低的问题，提出了一种基于时频图像和高次频谱特征联合的雷达信号识别算法。该算法首先对信号采用Choi-Williams分布（Choi-Williams distribution，CWD）变换获取时频图像，接着对时频图预处理并用灰度共生矩阵（gray level co-occurrence matrix，GLCM）提取纹理特征；然后利用对称Holder系数提取信号的高次频谱特征；再将纹理特征和高次频谱特征构成一组联合特征向量，最后通过支持向量机（support vector machine，SVM）实现雷达信号的分类识别。通过对8种典型雷达信号进行实验，结果表明本算法在信噪比为-8 dB时，不同信号的识别准确率能达到90%以上。

Abstract

Aiming at improving the accuracy of radar signal recognition under a low signal-to-noise ratio

a radar signal recognition algorithm based both on time-frequency image and high-order spectrum feature was proposed.Firstly

the time-frequency image was obtained by Choi-Williams distribution (CWD) transform

based on which the time-frequency image was preprocessed and the texture features were extracted by gray level co-occurrence matrix (GLCM) in sequence.Meanwhile

the symmetrical holder coefficient was used to extract the high-order spectral features of the signal.Then

the texture features and high-order spectrum features were form a new set of joint feature vectors.Finally

with the proposed feature vector the classification and recognition of radar signals were implemented by a support vector machine.The algorithm was verified on the data set with eight typical radar signals.Experimental results show that the recognition accuracy of different radar signals can achieve higher than 90% when the signal-to-noise ratio is -8 dB.

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references

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