Title :
A sparse Bayesian approach for joint SAR imaging and phase error correction
Author :
Chengguang Wu; Bin Deng; Hongqiang Wang; Yuliang Qin; Wuge Su
Author_Institution :
College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, China, 410073
Abstract :
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.
Keywords :
"Radar imaging","Synthetic aperture radar","Image reconstruction","Radar polarimetry","Bayes methods","Scattering"
Conference_Titel :
Computer Science and Network Technology (ICCSNT), 2015 4th International Conference on
DOI :
10.1109/ICCSNT.2015.7490986