A range-dependent autofocus algorithm based on the Tikhonov regularization method

Abstract

Phase Gradient Autofocus (PGA) is an algorithm developed to estimate phase errors in synthetic aperture radar (SAR) images. It is widely used in SAR image processing and has been shown to be very robust on a variety of imagery and phase error functions. Original PGA makes a narrow beam assumption, which is valid for most SAR systems. But it is known that the narrow beam assumption is not valid for large swath or low-altitude mode SAR, where range-dependent phase errors need to be compensated. Some PGA-based range-dependent autofocus algorithms were proposed to deal with this problem, but during raw data processing, we found that there are ill-posed problems in the linear motion error estimation model that will affect the accuracy of the estimation. In this article, combining the Tikhonov regularization (TR) method with phase gradient estimation, a novel autofocus algorithm is proposed to deal with the ill-posed problem in estimation. Real data processing demonstrates that the new method avoids the effect of parameter errors, estimates the range-dependent phase errors more precisely and finally leads to better focusing quality.