Recently, a novel design scheme of low-earth-orbit spaceborne mini-synthetic aperture radar (MiniSAR) system is proposed to exploit the integrated transceiver to collect the azimuth periodic block sampling data by using alternated transmitting and receiving operations. Because such collected data are downsampled, the images recovered by the typical matched filtering (MF)-based methods have the problems of obvious azimuth ambiguities, ghosts, and energy dispersion. To find a suitable method for such data, with the help of sparse signal processing technique, we first introduce sparse synthetic aperture radar (SAR) imaging with$\ell _{1}$-norm regularization-based approximated observation method to recover the large-scale considered scene. To further improve the imaging performance, a novel approximated observation unambiguous sparse SAR imaging method via$\ell _{2,1}$-norm is proposed. Compared with$\ell _{1}$-norm -based method, the recovered image by the proposed one achieves better imaging quality with reduced azimuth ambiguities and ghosts. Experimental results on simulated and real data validate the proposed method.

Different from optical image limited in time and space, sparse synthetic aperture radar (SAR) can obtain high-resolution image in all day and all weather conditions. Thus, SAR image has been widely used in military fields. With rapid development of technology and the growth of data volume, it is obviously impractical to interpret SAR image manually. Therefore, in this paper, we propose a novel sparse SAR automatic target recognition (ATR) framework, which is composed by the regularization sparse recovery algorithm and YOLO networks, so as to identify the SAR target quickly and accurately. In the proposed framework, we first use the complex approximate message passing (CAMP) based sparse image recovery algorithm to construct the sparse SAR image dataset, then identify the SAR targets by YOLOv3 and YOLOv4 networks. Experimental results based on MSTAR dataset validate the proposed framework effectively.