High-resolution ISAR imaging via MMV-based block-sparse signal recovery

Recently, compressed sensing (CS) methods are widely used in high-resolution inverse synthetic aperture radar (ISAR) imaging. However, these CS-based imaging methods generally do not take the block-sparse structure of the ISAR images into account, and the image recovery performance needs to be improved. By utilising the block-sparse structure of the signal, more sparse solution and better focused ISAR images can be obtained. In this study, the authors convert the block-sparse signal recovery problem into a sparse recovery problem for multiple measurement vector (MMV), which can be solved more efficiently. A sparser and more accurate solution can be obtained based on the MMV model, and therefore better focused ISAR images can be recovered. Simulation and experimental results validate the effectiveness of the proposed method.