基于双谱-谱图特征和深度卷积神经网络的HRRP目标识别方法

HRRP target recognition method based on bispectrum-spectrogram feature and deep convolutional neural network

针对雷达高分辨距离像(high resolution range profile,HRRP)目标识别中有效表示和特征提取这一关键问题,提出了基于双谱-谱图特征和深度卷积神经网络(deep convolution neural network,DCNN)的识别方法。首先,提取HRRP的双谱-谱图特征表示作为CNN的输入。然后,通过网络训练提取出深层本质特征,实现对雷达目标的识别。最后,对不同特征表示的识别结果进行对比。采用卫星目标实测数据进行实验,结果表明,该方法可以准确有效地识别雷达目标,而且与其他常用特征表示相比,双谱-谱图特征表示具有更好的识别准确率和噪声鲁棒性。

Aiming at the key problem of effective representation and extraction of features in radar high resolution range profile(HRRP)target recognition,a recognition method based on bispectrum-spectrogram feature and deep convolution neural network(DCNN)is proposed.Firstly,this method extracts the bispectrum-spectrogram feature representation of HRRP as the input of the CNN.Then,the deep and essential features are extracted by the network training and the radar targets can be recognized.Finally,the recognition results of different feature representations are compared.The experimental results of the satellite target measured data show that the method can recognize radar target effectively and accurately,and the bispectrum-spectrogram feature has better recognition accuracy and noise robustness than other commonly used HRRP feature representations.