Knowledge-aided Two-dimensional Autofocus for Spotlight SAR Polar Format Imagery

TL;DR

This paper introduces a knowledge-aided 2-D autofocus algorithm for Spotlight SAR polar format imagery that leverages prior knowledge of phase error structure to improve accuracy and efficiency, also applicable to moving target refocusing.

Contribution

It presents a novel 2-D autofocus method that exploits the analytical structure of phase errors, requiring only azimuth phase error estimates, enhancing robustness and computational efficiency.

Findings

Demonstrates improved autofocus accuracy in SAR imagery

Shows robustness in refocusing moving targets

Reduces computational complexity of 2-D autofocus

Abstract

Conventional two-dimensional (2-D) autofocus algorithms blindly estimate the phase error in the sense that they do not exploit any a priori information on the structure of the 2-D phase error. As such, they often suffer from low computational efficiency and lack of data redundancy to accurately estimate the 2-D phase error. In this paper, a knowledge-aided (KA) 2-D autofocus algorithm which is based on exploiting a priori knowledge about the 2-D phase error structure, is presented. First, as a prerequisite of the proposed KA method, the analytical structure of residual 2-D phase error in SAR imagery is investigated in the polar format algorithm (PFA) framework. Then, by incorporating this a priori information, a novel 2-D autofocus approach is proposed. The new method only requires an estimate of azimuth phase error and/or residual range cell migration, while the 2-D phase error can then be computed directly from the estimated azimuth phase error or residual range cell migration. This 2-D autofocus method can also be applied to refocus moving targets in PFA imagery. Experimental results clearly demonstrate the effectiveness and robustness of the proposed method.