A Sparse Bayesian Approach for SAR Imaging with Compensation of Observation Position Error

Compressive sensing (CS) has been successfully used in synthetic aperture radar (SAR) imaging and shows the great potential. However, the existing CS-based SAR models assume the exact mathematical model of the observation process. In practice, the inaccuracy in the observation model will cause various degradation in the reconstructed SAR images, especially in the frequencies of millimeter-wave or terahertz-waves. In this paper, a method is proposed to compensate the observation position errors in CS-based radar imaging. It uses an iterative algorithm, which cycles through steps of target reconstruction and observation position error estimation. A sparse Bayesian recovering method named the expansion-compression variance-component based method (ExCoV) is used for image reconstruction. The proposed method can estimate the observation position errors accurately, and the reconstruction quality of the target images can be improved significantly. Simulation results show the effectiveness of the proposed method.