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

作　　者：汪思源 曲毅[1] 陈怡君 WANG Siyuan;QU Yi;CHEN Yijun(College of Information Engineering,Engineering University of PAP,Xi’an 710086,China)

机构地区：[1]武警工程大学信息工程学院,西安710086

出　　处：《空军工程大学学报》2024年第3期77-85,共9页Journal of Air Force Engineering University

基　　金：国家自然科学基金(62131020)。

摘　　要：基于轨道角动量模态变量与目标方位角变量的近似对偶关系,涡旋电磁波雷达可以实现对静止目标的二维高分辨成像,然而目标回波中的贝塞尔函数项会严重影响方位角向聚焦性能。现有基于逆投影算法的贝塞尔函数补偿方法计算量很大,难以实际应用。针对上述问题,提出一种利用U-Net卷积神经网络抑制贝塞尔函数影响、实现涡旋电磁波雷达高分辨成像的方法。首先,根据雷达目标散射分布的稀疏特性对U-Net网络进行改进,然后对目标回波信号进行二维快速傅里叶变换预处理得到目标散焦图像,将其作为改进U-Net网络的输入,并将目标理想电磁散射模型作为网络输出对网络进行监督训练。最后,基于未知目标回波信号,将预处理后的目标散焦图像输入到训练完备的网络模型中,即可得到聚焦良好的高分辨成像结果。仿真实验证明,该成像方法能够有效提高目标成像聚焦性能,且该网络模型在噪声存在的情况下仍具有较好的泛化能力。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 

分 类 号：TN957[电子电信—信号与信息处理]