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.