Multistatic ISAR Imaging based on Nonconvex Constraint and Nonlocal Total Variation Regularization

In this paper, a novel framework based on nonconvex constraint and nonlocal total variation (NLTV) regularization is proposed for sparsity-driven multistatic inverse synthetic aperture radar (ISAR) imaging under limited measurements. The nonconvex constraint measures sparsity more appropriately since it can better fit to the l 0 norm, and NLTV plays a better role in preserving the shape and geometry of target than the commonly used scattering points sparsity. An iterative optimization solution method for the proposed imaging model is also derived. Simulation results show the effectiveness of the proposed method.