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

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。

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 pro-posed 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.