A 3-D Imaging Method Based on Sparsity Adaptive Matching Pursuit for Multi-baseline Circular SAR

Recently, the synthetic aperture radar (SAR) three-dimensional (3-D) imaging technology has been widely developed in both military and civilian fields. The multi-baseline (MB) circular SAR (CSAR) combines the advantages of the SAR tomography and CSAR, showing great capabilities of the 3-D reconstruction of the target scene. In this paper, we proposed a 3-D imaging method based on sparsity adaptive matching pursuit (SAMP) for the MB CSAR system. Firstly, a circular aperture is divided into multiple sub-apertures, and the back-projection algorithm (BPA) is applied to form the sub-aperture 2-D images. Secondly, the phase gradient autofocus (PGA) and SAMP are performed to form sub-aperture 3-D images. Finally, all sub-aperture 3-D images are incoherently accumulated to form the final 3-D CSAR image. The experimental results based on the Gotcha dataset validate the feasibility of the proposed method.