空军工程大学学报2024，Vol.25Issue(3)：77-85,9.DOI:10.3969/j.issn.2097-1915.2024.03.012
基于U-Net的涡旋电磁波雷达成像方法
A Vortex Electromagnetic Radar Imaging Method Based on U-Net
摘要
Abstract
Based on the approximate dual relationship between the orbital angular momentum modal varia-ble and the target azimuth variable,vortex electromagnetic wave radar can achieve two-dimensional high-resolution imaging of stationary targets,but the Bessel function term in the target echo is able to seriously affect the azimuth focusing performance.The existing Bessel function compensation method based on in-verse projection algorithm is computationally intensive and difficult to apply in practice.In view of the a-bove-mentioned problems,this paper proposes a method by utilizing U-Net convolutional neural network for suppressing the influence of Bessel function and realizing high-resolution imaging of vortex electromag-netic wave radar.Firstly,the U-Net network is improved according to the sparse characteristics of radar targets in the observation space,and on the basis of this,the target defocus image is obtained by two-di-mensional fast Fourier transform preprocessing of the target echo signal,and the target defocus image is further used as an input of the improved U-Net network,and the target ideal electromagnetic scattering model is used as the network output to supervise and train the network.Finally,based on the unknown target echo signal,the preprocessed target defocus image is input to the well-trained network model,and the well-focused high-resolution imaging results can be obtained.The simulation experiments show that the proposed method can effectively improve the focusing performance of target imaging,and the network model still is good in generalization ability in the presence of noise.关键词
涡旋电磁波雷达/U-Net卷积神经网络/轨道角动量/贝塞尔函数Key words
vortex electromagnetic radar/orbital angular momentum/Bessel function/U-Net convolution neural network分类
信息技术与安全科学引用本文复制引用
汪思源,曲毅,陈怡君..基于U-Net的涡旋电磁波雷达成像方法[J].空军工程大学学报,2024,25(3):77-85,9.基金项目
国家自然科学基金(62131020) （62131020）
