基于Choi-Williams时频图像特征的雷达辐射源识别

针对复杂体制雷达辐射源识别,提出一种基于Choi-Williams时频图像的雷达辐射源信号特征提取和识别方法,将信号识别转化为图像识别问题。首先对雷达辐射源信号进行Choi-Williams时频变换,将得到的时频图转化为灰度图像;然后采用一系列图像处理方法对时频图像进行增强和去噪,之后将灰度图像转化为二值图像,并剪切掉不含信号的图像区域;最后分别提取二值图像的中心矩和伪Zernike矩作为识别特征,并采用支持向量机分类器实现信号的分类识别。文中针对8种常见雷达信号识别进行了仿真实验,结果表明在较大的信噪比范围内,该方法能获得较为满意的识别率,其中当信噪比为-3dB时,采用伪Zernike矩特征平均识别率仍能达到92%,验证了所提出方法的有效性。

Radar Emitter Recognition Based on the Image Feature of Choi-Williams Time-frequency Distribution

To correctly classify advanced radar emitter signals,a novel approach using the image feature of Choi-Williams time-frequency distribution for radar emitter signal recognition is proposed.It transforms the classification of emitter signals into image processing and image recognition.Time-frequency images of radar emitter signals are obtained by using Choi-Williams distribution,and then these images are transformed into grayscale ones.A series of image processing methods are adopted for time-frequency image enhancement and de-noising.After that,the grayscale images are converted into binary images.Moreover,the areas which do not contain signal components are removed from the edges of the image.Finally,the centralize moments and pseudo-zernike moments are calculated as the feature for signal recognition,and the support vector machine is used to identify radar emitter signals automatically.Simulation results show that the proposed approach can achieve satisfying recognition accurately when signal-to-noise rate(SNR) varies in a large range.Even for SNR=-3 dB,the proposed method which adopts pseudo-Zernike moments works effectively as high as 92% recognition rate.The validity of the approach is demonstrated by experiments.