In this research, the authors extract features from intermediate frequency band radar signals in the time-frequency domain for classification. The extracted features are classified via support vector machine and K-nearest neighbour classifiers. They show the accuracy of classification is above 99% for different classes of radar signals except for frequency shift keying signal with accuracy 83% in negative signal-to-noise ratio (SNR). To identify the radars with the same class, the classification accuracy is 91% for SNR between 5 to 15 dB and 64% in the worst case for SNR between -1 to 10 dB. The proposed method is compared with some methods based on the empirical mode decomposition (EMD), cumulant and Zhao Atlas Mark Distribution (ZAMD). The results show that the classification error in the proposed method is less than that of EMD method 55% in the best case and 9% in the worst case. The performance of the cumulant-based method is weaker than that of the proposed method in common designed scenarios becoming almost similar only in one scenario. The ZAMD-based method could only distinguish the signals with different modulations in high SNR while it is unable to classify the signals with the same modulation but different parameters.

中文翻译：

---

利用能量-时间-频率分布特征对雷达信号进行分类

在这项研究中，作者从时频域的中频雷达信号中提取特征进行分类。提取的特征通过支持向量机和K近邻分类器进行分类。他们显示，除频移键控信号的负信噪比（SNR）精度为83％以外，不同类别的雷达信号的分类精度均高于99％。为了识别具有相同类别的雷达，对于5至15 dB的SNR，分类精度为91％；对于-1至10 dB的SNR，最差情况为64％。将该方法与基于经验模态分解（EMD），累积量和赵阿特拉斯马克分布（ZAMD）的方法进行了比较。结果表明，该方法的分类误差在最佳情况下小于EMD方法的55％，在最坏情况下为9％。在常规设计的方案中，基于累积量的方法的性能比所提​​出的方法的性能弱，仅在一种方案中变得几乎相似。基于ZAMD的​​方法只能在高SNR中区分具有不同调制的信号，而无法对具有相同调制但参数不同的信号进行分类。