Knowledge-aided two-dimensional autofocus for synthetic aperture radar

Conventional two-dimensional (2-D) autofocus algorithms blindly estimate the phase error in the sense that they do not take advantages of the known structure of the phase error. As such, they do not achieve accurate estimation of the 2-D phase error due to lack of data redundancy. In this paper, we propose a novel 2-D autofocus algorithm that exploits the phase structure information, which is known a priori. The proposed method is described in two parts. We first reveal the analytical structure of the 2-D residual phase error in synthetic aperture radar (SAR) imageries under the polar format algorithm framework. Based on this result, the proposed 2-D autofocus approach converts the 2-D phase error estimation problem into a one-dimensional (1-D) problem of azimuth phase error estimation. Based on the phase structure, the 2-D phase error can then be computed from the estimated azimuth phase error. Experimental results clearly demonstrate the effectiveness of the proposed method.