Synthetic Aperture Radar Autofocus Based on a Bilinear Model

Abstract—Autofocus algorithms are used to restore images in nonideal synthetic aperture radar imaging systems. In this paper, we propose a bilinear parametric model for the unknown image and the nuisance phase parameters and derive an efficient max-imum-likelihood autofocus (MLA) algorithm. In the special case of a simple image model and a narrow range of look angles, MLA co-incides with the successful multichannel autofocus (MCA). MLA can be interpreted as a generalization of MCA to a larger class of models with a larger range of look angles. We analyze its ad-vantages over previous extensions of MCA in terms of identifia-bility conditions and noise sensitivity. As a byproduct, we also pro-pose numerical approximations to the difficult constant modulus quadratic program that lies at the core of these algorithms. We demonstrate the superior performance of our proposed methods using computer simulations in both the correct and mismatched system models. MLA performs better than other methods, both in terms of the mean squared error and visual quality of the restored image. Index Terms—Autofocus, Fourier-domain multichannel auto-focus (FMCA), maximum-likelihood estimation, multichannel autofocus (MCA), phase gradient autofocus (PGA), semi-definite relaxation (SDR), sharpness-maximization autofocus, spotlight-mode synthetic aperture radar (SAR), successive cancel-lation approach (SCA). I