Journal of Computer and Communications
Volume 4, Issue 3 (March 2016)
ISSN Print: 2327-5219 ISSN Online: 2327-5227
Google-based Impact Factor: 1.12 Citations
Radar Imaging of Sidelobe Suppression Based on Sparse Regularization ()
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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.
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