290 / 2020-01-05 15:55:00

An Accurate Sparse Synthetic Aperture Radar Imaging Method Based on Debiased L1 Regularization

Sparse signal processing has been applied in synthetic aperture radar (SAR) imaging. As a typical sparse reconstruction model, L1 regularization often underestimates the intensities of the targets. The estimated radar cross section (RCS) is related to the pixel intensity. Thus, the linear relationship between the targets' intensities cannot be kept. To avoid the underestimation, a debiased step is needed.
A common debias method is to implement least squares (LS) on the support of the L1 estimation. Azimuth-range decouple operators are often used in sparse SAR imaging. It can reduce the memory and computational costs and makes the large scale scenario imaging realistic. In this paper, the azimuth-range decouple operators based LS are deducted. The performance of the proposed method is verified using the real data experiment.