A Nonquadratic Regularization-based Technique for Joint SAR Imaging and Model Error Correction

Regularization based image reconstruction algorithms have successfully been applied to the synthetic aperture radar (SAR) imaging problem. Such algorithms assume that the mathematical model of the imaging system is perfectly known. However, in practice, it is very common to encounter various types of model errors. One predominant example is phase errors which appear either due to inexact measurement of the location of the SAR sensing platform, or due to effects of propagation through atmospheric turbulence. We propose a nonquadratic regularization-based framework for joint image formation and model error correction. This framework leads to an iterative algorithm, which cycles through steps of image formation and model parameter estimation. This approach offers advantages over autofocus techniques that involve postprocessing of a conventionally formed image. We present results on synthetic scenes, as well as the Air Force Research Labarotory (AFRL) Backhoe data set, demonstrating the effectiveness of the proposed approach.