A novel autofocus imaging method for ISAR based on compressive sensing

In this paper, a novel autofocus imaging method is proposed to achieve high-resolution for inverse synthetic aperture radar (ISAR) in the compressive sensing (CS) framework. Firstly, we fomulate the ISAR CS imaging in a Multiple Measurement Vector (MMV) sparse optimization problem. Then, by utilizing the structure sparsity of ISAR image, i.e. row sparsity and column sparsity simutaneously, our method is derived. Compared with traditional imaging methods using Single Measurement Vector (SMV) signal mode, the proposed method further combines the joint sparse information of target image, therefore can improve the autofocus imaging quality efficiently. Experimental results verify the effectiveness of the proposed method.