Study on the Sparse Sub-block Microwave Imaging Based on Lasso(In English)

Sparse microwave imaging requires nonlinear algorithm that is expensive for large scene imaging. Therefore, the sub-block imaging method is studied, in which the measured data and the relative imaging region is divided into sub-blocks, and then sparse microwave imaging algorithm based on 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. Compared to the overall reconstruction of the sparse scene, sub-block algorithm can control data amount involved in each reconstruction, so as to avoid the signal processor frequently accessing the disk, which will cost huge time. Indeed, 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 of that of the overall reconstruction. The result proved by simulation and real data processing supports the validity of our method.