Three-Dimensional Array SAR Sparse Imaging Based on Hybrid Regularization

With the development and maturity of compressed sensing theories, sparse signal processing has been widely applied to synthetic aperture radar (SAR) imaging.
${L}_{{1}}$
regularization, as an effective sparse reconstruction model, can enhance the sparsity of the scene. However, due to the convexity of the
${L}_{{1}}$
regularization function, the
${L}_{{1}}$
-based sparse reconstruction frequently introduces bias estimation, resulting in an underestimation of target amplitudes. Furthermore, the 3-D SAR imaging scene has diverse features, which are difficult to characterize solely with the
${L}_{{1}}$
regularization. Therefore, in this article, we propose a novel imaging framework for 3-D array SAR imaging, which combines a hybrid regularization function and an improved variable splitting with the method of multiplier (IVSMM). First, we present the hybrid regularization function, which combines smoothly clipped absolute deviation (SCAD) and high-dimensional total variation (HTV) to reduce bias effects and preserve the regional features of the target. Then, we present IVSMM to solve the optimization problem of hybrid regularization, effectively reducing the computational complexity required for 3-D imaging. Finally, the excellent reconstruction performance of the proposed method is validated through a series of simulations and experimental data.