L2.1/2-norm Regularization Based Unambiguous Sparse SAR Imaging

Sparse signal processing technique has shown signif-icant potential in high-resolution synthetic aperture radar (SAR) imaging. Compared with typical matched filtering (MF) based method, it can obtain high-quality image of sparse surveillance region from down-sampled echo data. In this paper, with the help of approximated observation technology, we propose an
$L_{2,1/2}$
-norm regularization based sparse SAR imaging method to process the echo data of large-scale sparse scene. Compared with typical
$L_{q}$
-norm
$(0 < q\leq 1)$
regularization based method, it can well suppress the azimuth ambiguity caused by the down-sampling. While compared with
$L_{2,1}$
-norm regularization based sparse SAR imaging algorithm, the proposed method can further improve the performance of recovered image and hence obtain the well focused sparse SAR image.