Radar Imaging of Sidelobe Suppression Based on Sparse Regularization  

作　　者：Xiaoxiang Zhu Guanghu Jin Feng He Zhen Dong Xiaoxiang Zhu;Guanghu Jin;Feng He;Zhen Dong(Institute of Space Electronic and Information Technology, School of Electronic Science and Engineering, National University of Science and Technology, Changsha, China)

机构地区：[1]Institute of Space Electronic and Information Technology, School of Electronic Science and Engineering, National University of Science and Technology, Changsha, China

出　　处：《Journal of Computer and Communications》2016年第3期108-115,共8页电脑和通信（英文）

摘　　要：Synthetic aperture radar based on the matched filter theory has the ability of obtaining two-di- mensional image of the scattering areas. Nevertheless, the resolution and sidelobe level of SAR imaging is limited by the antenna length and bandwidth of transmitted signal. However, for sparse signals (direct or indirect), sparse imaging methods can break through limitations of the conventional SAR methods. In this paper, we introduce the basic theory of sparse representation and reconstruction, and then analyze several common sparse imaging algorithms: the greed algorithm, the convex optimization algorithm. We apply some of these algorithms into SAR imaging using RadBasedata. The results show the presented method based on sparse construction theory outperforms the conventional SAR method based on MF theory.Synthetic aperture radar based on the matched filter theory has the ability of obtaining two-di- mensional image of the scattering areas. Nevertheless, the resolution and sidelobe level of SAR imaging is limited by the antenna length and bandwidth of transmitted signal. However, for sparse signals (direct or indirect), sparse imaging methods can break through limitations of the conventional SAR methods. In this paper, we introduce the basic theory of sparse representation and reconstruction, and then analyze several common sparse imaging algorithms: the greed algorithm, the convex optimization algorithm. We apply some of these algorithms into SAR imaging using RadBasedata. The results show the presented method based on sparse construction theory outperforms the conventional SAR method based on MF theory.

关 键 词：Matched Filtering Sparse Representation Sparse Reconstruction Convex Optimization Greed Algorithm 

分 类 号：TN9[电子电信—信息与通信工程]