A Coarse-to-Fine Autofocus Approach for Very High-Resolution Airborne Stripmap SAR Imagery

Abstract

An autofocus operation is an indispensable procedure to obtain well-focused images for synthetic-aperture radar (SAR) systems without precise navigation devices. Three challenges have been faced in the very high-resolution (VHR) airborne SAR autofocus due to the long cumulative time: the varying along-track velocity, the residual range cell migration (RCM), and the range-dependent phase errors with higher order components. When it comes to the stripmap mode, the autofocus becomes more complicated, since the scenario with a few strong scatterers is more likely to be encountered with a moving beam. Combining the merits of parametric and nonparametric autofocus algorithms, a robust motion error estimation method is proposed in this paper. First, we perform a stripmap multiaperture mapdrift autofocus operation to extract the along-track velocity and the most range-invariant errors, removing the residual RCM at a subaperture scale. Second, one referential center block is selected to retrieve the residual range-invariant error, which can eliminate the residual RCM globally in the range dimension. With a global high-quality input, the residual range-variant phase errors can be retrieved precisely utilizing a center-to-edge local maximum-likelihood weighted phase gradient autofocus kernel at last. Experiments on real VHR airborne stripmap SAR data are performed to demonstrate the robustness of the proposed method.