传统的基于高分辨距离像（high resolution range profile,HRRP）雷达目标识别方法易受噪声影响，为此提出一种基于时频分析与深度学习的HRRP雷达目标识别方法。首先使用生成模型对低信噪比的HRRP数据进行处理，以提高数据的信噪比，生成模型采用深度卷积生成对抗网络（deep convolutional generative adversarial network,DCGAN）和所提出的约束朴素最小二乘生成对抗网络（constrained naive leasts quares generative adversarial network,CN-LSGAN）；其次将处理后的数据分别进行短时傅里叶变换（short-time Fourier transform,STFT）和小波变换（wavelet transform,WT），得到二维时频数据；最后利用卷积神经网络（convolutional neuralnetwork,CNN）进行识别。实验结果表明，CN-LSGAN相对DCGAN能够更好地提高信噪比，WT相比STFT得到的数据更能获取HRRP特征信息，因而基于CN-LSGAN,WT与CNN的HRRP雷达目标识别方法具有更好的识别效果。
In view of the problem that radar target recognition methods based on traditional high resolution range profile (HRRP) are susceptible to noise, an HRRP radar target recognition method employing time-frequency analysis and deep learning is proposed. First, low signal-to-noise ratio HRRP data is processed, and gains an improved signal-to-noise ratio by using a generative model which uses deep convolutional generative adversarial network (DCGAN) and constrained naive least squares generative adversarial network (CNLSGAN) proposed in this paper. Second, the processed data is processed with short-time Fourier transform (STFT) and wavelet transform (wavelet transform, WT) respectively to obtain two-dimensional time-frequency data. Finally, the obtained two-dimensional data is recognized by convolutional neural network (CNN). Experimental results show that the proposed CN-LSGAN performs better in improving signal-to-noise ratio compared to DCGAN, and WT can obtain HRRP feature information more efficiently than STFT. Therefore, the HRRP radar target recognition method based on CN-LSGAN, WT and CNN has higher recognition ability.
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