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

作　　者：聂江华 肖永生[1,2] 黄丽贞[1] 贺丰收 NIE Jianghua;XIAO Yongsheng;HUANG Lizhen;HE Fengshou(College of Information Engineering,Nanchang Hangkong University,Nanchang 330063,Jiangxi,China;College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,Jiangsu,China;Leihua Electronic Technology Research Institute,Aviation Industry Corporation of China,Ltd.,Wuxi 214063,Jiangsu,China)

机构地区：[1]南昌航空大学信息工程学院,江西南昌330063 [2]南京航空航天大学电子信息工程学院,江苏南京211106 [3]中国航空工业集团公司雷华电子技术研究所,江苏无锡214063

出　　处：《应用科学学报》2022年第6期973-983,共11页Journal of Applied Sciences

基　　金：国家自然科学基金(No.61661035,No.62261040);江西省自然科学基金(No.20192BAB207001);航空科学基金(No.201920056001,No.20200020056001);江西省研究生创新专项资金(No.YC2020-S519)资助。

摘　　要：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.

关 键 词：雷达目标识别 高分辨距离像 约束朴素最小二乘生成对抗网络 深度卷积生成对抗网络 时频分析 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]