摘要
在目标近场散射特性的研究中,通常采用从目标的二维图像中反演RCS的方法,因此毫米波全息成像直接决定了目标RCS的准确性。现下大多采用以傅里叶变换为理论基础的成像算法处理目标的散射回波信号,然而该算法采样点数过多,需要加窗或变迹滤波来抑制图像旁瓣,导致过程较为复杂。由于压缩感知技术可以在去除冗余数据的基础上,利用观测矩阵和重构算法恢复出较为精确的原始图像,在效率和精度方面都能得到有效提高,因此本文将其应用到近场目标的RCS成像诊断中,通过改进的L_(1/2)迭代阈值算法建立目标的二维图像,进而反演目标的RCS,后续的仿真和实验结果验证了该算法的可行性。
In the study of the near-field scattering characteristics of targets,the method of inverting RCS from the twodimensional image of the target is usually used,so millimeter wave holographic imaging directly determines the accuracy of the target RCS.Currently,most imaging algorithms based on Fourier transform are used to process the scattered echo signal of targets.However,this algorithm has too many sampling points and requires windowing or apodization filtering to suppress image sidelobes,resulting in a more complex process.Because compressed sensing technology can recover more accurate original images by using observation matrix and reconstruction algorithm on the basis of removing redundant data,and can effectively improve efficiency and accuracy,this paper applies it to RCS imaging diagnosis of near-field targets through improved L_(1/2) iterative threshold algorithm establishes a two-dimensional image of the target,and then inverts the RCS of the target.Subsequent simulation and experimental results verify the feasibility of this algorithm.
作者
王斌
朱莉
刘依纯
WANG Bin;ZHU Li;LIU Yi-chun(Department of Detection and Control Engineering,School of Electronic Engineering and Optical Technology,Nanjing University of Science&Technology,Nanjing 210094,China)
出处
《微波学报》
CSCD
北大核心
2023年第S01期338-343,共6页
Journal of Microwaves
关键词
毫米波
近场散射特性
RCS成像诊断
压缩感知技术
millimeter wave
near field scattering characteristics
RCS imaging diagnosis
compressive sensing
