Study on the Sparse Sub-block Microwave Imaging Based on Lasso

Abstract

Sparse microwave imaging requires a nonlinear algorithm that is expensive for large scene imaging. Therefore, the sub-block imaging method, in which the measured data and the relative imaging region are divided into sub-blocks, is studied. Then, a sparse microwave imaging algorithm based on the Least absolute shrinkage and selection operator (Lasso) is performed on each sub-block. Finally, the sub-blocks are combined to obtain the whole image of the large scene. When compared with the overall reconstruction of the sparse scene, the sub-block algorithm can control the amount of data involved in each reconstruction, thereby avoiding frequent accessing of the disk by the signal processor, which is time consuming. Further, the theoretical analysis illustrates that the sub-block sparse imaging method is also accurate and stable, and the associated reconstruction error is no more than two times that of the overall reconstruction. The simulation and real data processing results support the validity of our method.