L2,1-norm Regularization Based Sparse SAR Imaging From Periodic Block Sampling Data: Initial Result

In order to avoid the receiver saturation, the low-earth-orbit spaceborne Mini-synthetic aperture radar (MiniSAR) system uses the integrated transceiver for data collection. However, this kind of transceiver can be in one state of transmitting or receiving only at any time, which will cause the periodic loss of collected data. Due to such down-sampled data, the typical matched filtering (MF) based algorithms could not properly reconstruct the scene of interest. Thus new SAR imaging method should be studied to solve this problem. In this paper, an L-2,L-1-norm regularization based sparse SAR imaging method is proposed to process the azimuth periodic block sampling data. Because the ghosting terms caused by the periodic block sampling are considered in the scene recovery, it can effectively suppress the ghosts to achieve high-resolution sparse scene reconstruction. Initial experimental results on simulated and real data are shown to support our viewpoint.