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has been cited by the following article:

• TITLE: Radar Imaging of Sidelobe Suppression Based on Sparse Regularization

  AUTHORS: Xiaoxiang Zhu, Guanghu Jin, Feng He, Zhen Dong

  KEYWORDS: Matched Filtering, Sparse Representation, Sparse Reconstruction, Convex Optimization, Greed Algorithm

  JOURNAL NAME: Journal of Computer and Communications, Vol.4 No.3, March 2, 2016

  ABSTRACT: 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.