Improved SAR imaging algorithm with azimuth periodically missing data - Yang, Weixing; Bi, Hui; Zhu, Daiyin (IET radar, sonar & navigation, 2020)
Aiming at the periodically missing data of synthetic aperture radar (SAR) in azimuth, a reconstruction and imaging algorithm based on ${\bi L}_p$Lp-alternating direction method with compressed sensing theory is proposed. The algorithm can effectively suppress ghosting and aliasing caused by azimuth missing data, and improve the imaging quality. To reduce memory consumption and lower computational complexity, approximate observation model based on SAR raw data simulator is utilised to rapid reconstruction imaging. Compared to the traditional iterative shrinkage thresholding algorithm, the proposed algorithm has better reconstruction image quality. Simulation and raw SAR echo data processing demonstrate the effectiveness of the proposed method in solving the problem of imaging with periodically missing data in SAR azimuth.