A Sparse Bayesian Approach for Joint SAR Imaging and Phase Error Correction

SAR image formation algorithms have implicit or explicit dependence on the mathematical model of the image observation process. Inaccuracies in the image model will bring phase error, which may cause various quality degradations in the reconstructed images, especially in the millimeter-wave or terahertz-waves radar. In this paper, we propose a sparse Bayesian approach for joint SAR imaging and phase error correction. It uses an iterative algorithm, which cycles through steps of target reconstruction and phase error estimation. A sparse Bayesian recovering method, which named the expansion-compression variance-component based method (ExCoV), is used for image reconstruction. The proposed method can significantly improve the quality of the reconstructed image, and the phase errors can be estimated accurately. Simulation results show the effectiveness of the proposed method.