SAR Ground Moving Target Imaging based on Sparse Representation

Abstract

synthetic aperture radar (SAR) for ground moving target indication (GMTI) and imaging (GMTIm) have been gaining increasing interests for both civilian and military applications. Because SAR is generally designed for imaging a stationary scene, the SAR image of a moving target will be both displaced and smeared.More specifically, by exploiting the inherent sparsity of the moving targets in the clutter-suppressed SAR image domain, in this article. the intended SAR-GMTIm problem is solve by a sparse Bayesian perspective.The theory of CS has been successfully applied to SAR/ISAR imagery to achieve high cross-range resolution with a limited number of pulsesIn order to evaluate the quality of images, we apply the target-to-clutter ratio (TCR), which is commonly used in syntheticaperture radar (SAR) image assessment.The proposed algorithm shows a 10-dB higher TCR compared to the conventional algorithm.

Keywords

• : synthetic aperture radar (sar)
• ground moving target indication (gmti)
• ground moving target imaging (gmtim)
• sparse bayesian learning(sbl)
• lv’s distribution (lvd)
• vb-em