In synthetic aperture radar (SAR) imaging, perturbations in the motion of the moving platform induce an undesired phase error due to imprecise knowledge of the motion, which results in the significant degradations in the quality of SAR images. In this paper, we present an efficient compressive sensing (CS)-based SAR imaging integrated with autofocus technique. The novel approach is based on an estimation-theoretic l 1 -norm-based approach coupled with Tikhonov-type regularization which combines an observation model of the SAR image formation process with the CS reconstruction problem of the SAR image regarding the sparsity. The optimization problem derived by considering spatially variant phase errors along azimuth domain and the dataset sampled at low rates can be effectively addressed by an efficient iterative method, wherein each iteration both SAR image formation and phase error correction are simultaneously carried out. The simulations and experimental results are presented to validate the effectiveness of the proposed method in terms of reliable image recovery and efficient computational complexity.