Time-frequency image and high-order spectrum characteristics based radar signal recognition

Shitong LI; Daying QUAN; Zeyu TANG; Yun CHEN; Xiaofeng WANG; Xiaoping JIN

doi:10.11959/j.issn.1000-0801.2022024

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Time-frequency image and high-order spectrum characteristics based radar signal recognition

Research and Development | 更新时间：2024-06-05

- Time-frequency image and high-order spectrum characteristics based radar signal recognition
- Telecommunications Science Vol. 38, Issue 2, Pages: 84-91(2022)
- 作者机构：
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- DOI：10.11959/j.issn.1000-0801.2022024
  CLC： TP393
- Published Online：2022-02，
  
  Published：20 February 2022
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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：

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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