基于知识的Spotlight SAR极坐标格式图像二维的自聚焦
Knowledge-aided Two-dimensional Autofocus for Spotlight SAR Polar Format Imagery
摘要：
传统的二维的【形】（2-D）自动聚焦算法在它们不利用关于2-D相位误差的结构的任何先验信息的意义上盲估计相位误差。因此，它们通常遭受低计算效率和缺乏数据冗余以准确地估计2-D相位误差。本文提出了一种基于二维相位误差结构先验知识的知识辅助二维自聚焦算法。首先，作为所提出的KA方法的先决条件，在极坐标格式算法（PFA）的框架下，在SAR图像中的残余二维相位误差的解析结构进行了研究。然后，通过将此先验信息，提出了一种新的2-D自动聚焦方法。该方法只需要估计方位相位误差和/或剩余距离单元偏移，而二维相位误差则可以直接由估计的方位相位误差或剩余距离单元偏移计算。这种二维自聚焦方法也可以应用于PFA图像中运动目标的重聚焦。实验结果清楚地证明了所提出的方法的有效性和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
