Sparse Reconstruction for Synthetic Aperture Radar based on Split SPICE

Synthetic aperture radar (SAR) technology is widely applied to observing the ground surface. Recently, the sparse iterative covariance-based estimator (SPICE) method has been developed for SAR to obtain a higher resolution and lower sidelobes of the sparse targets over conventional fast Fourier transform (FFT)-based method. However, the matrix inversion operation is required for each iteration, which results notably high computational complexity. Aiming at this problem, a split SPICE method is proposed to improve the computational efficiency. The algorithm is derived from the equivalence between the SPICE and the weighted square root least absolute shrinkage and selection operator (LASSO) problem. Furthermore, the problem is split into three sub-problems by introducing an auxiliary variable. The proposed split SPICE method has the advantages of higher computing efficiency over conventional SPICE algorithm. Moveover, compared with conventional Split Bregman method, the proposed method requires less user parameters. Simulations verify the excellent performance of the proposed method.