系统工程与电子技术 ›› 2018, Vol. 40 ›› Issue (2): 303-307.doi: 10.3969/j.issn.1001-506X.2018.02.10

• 传感器与信号处理 • 上一篇    下一篇

基于奇异值熵和分形维数的雷达信号识别

曲志昱, 毛校洁, 侯长波   

  1. 哈尔滨工程大学信息与通信工程学院, 黑龙江 哈尔滨 150001
  • 出版日期:2018-01-25 发布日期:2018-01-23

Radar signal recognition based on singular value entropy and fractal dimension

QU Zhiyu, MAO Xiaojie, HOU Changbo   

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
  • Online:2018-01-25 Published:2018-01-23

摘要: 针对低信噪比条件下雷达信号脉内调制方式识别算法识别率低的问题,提出了基于奇异值熵和分形维数的雷达信号识别算法。该方法首先通过ChoiWilliams分布得到信号的时频图像,提取时频图像的奇异值熵;然后再提取信号频谱的盒维数与信息维数,组成三维特征向量;最后使用基于支持向量机的分类器实现雷达信号的分类识别。对8种典型雷达信号的仿真试验结果表明该方法抗噪性强、识别率高,在信噪比大于1 dB时,平均识别率能达到95%以上。

Abstract: To solve the problem of the low recognition rate of radar signal pulse modulation method in low signal-to-noise ratio (SNR), a method of radar signal recognition based on singular value entropy and fractal dimension is proposed. First, the timefrequency image of the signal is obtained by Choi-Williams distribution, and the singular value entropy of the time-frequency image is extracted. Then the box dimension and the information dimension of the signal spectrum are extracted to form the three-dimension feature vector. Finally, a classifier based on support vector machine is used to realize the classification and recognition of radar signals. The simulation results of eight typical radar signals show that the proposed method is robust to noise and has a high recognition rate. When the SNR is higher than 1dB, the average recognition rate can reach above 95%.

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