基于时频分析与深度学习的高分辨距离像雷达目标识别

High Resolution Range Profile Radar Target Recognition Based on Time-Frequency Analysis and Deep Learning

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 least squares 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 neural network,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.