The polar format algorithm (PFA) is a frequency-domain algorithm with high computational efficiency especially for the high resolution imaging applications in wide-angle staring synthetic aperture radar (WAS-SAR). Because of the image distortion and defocus caused by plane wave assumption, the effective imaging scene is limited to a small region which is defined as the defocus-negligible region. In this article, a novel sub-block imaging algorithm which first applied to WAS-SAR is proposed. This method combines sub-block imaging and spatially variant post-filtering (SVPF) which can not only effectively extend the depth of focus of PFA but also greatly reduces the computational burden. What's more, the method of mosaic image is proposed in this article which can eliminate discontinuity between adjacent sub-blocks. First, the large scene is segmented into multiple regions along range direction and the size of each region is still larger than that of the defocus-negligible region. Second, refocusing is implemented by a phase compensation of the already de-chirped signal with the center of sub-blocks, respectively, and each sub-block image is formed by PFA. Third, the region in each sub-block that exceeds the defocus-negligible region is corrected by SVPF and the image distortion can be corrected by 2-D interpolation. Finally, the mosaic image method is applied to get the large-scene image with high quality. The algorithm proposed in this article is efficient and solves the problems in the process of SAR image mosaic such as dislocation and grayscale differences. The simulated and experimental data results demonstrate the effectiveness and practicability of the proposed algorithm.

中文翻译：

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用于大场景高分辨率 WAS-SAR 成像的扩展极坐标格式算法

极坐标格式算法（PFA）是一种计算效率高的频域算法，特别适用于广角凝视合成孔径雷达（WAS-SAR）中的高分辨率成像应用。由于平面波假设引起的图像失真和散焦，有效的成像场景被限制在一个小区域内，该区域被定义为离焦可忽略区域。本文提出了一种首次应用于WAS-SAR的新型子块成像算法。该方法结合了子块成像和空间变异后滤波（SVPF），不仅可以有效扩展PFA的焦深，而且大大减轻了计算负担。更重要的是，本文提出了马赛克图像的方法，可以消除相邻子块之间的不连续性。第一的，大场景沿范围方向被分割成多个区域，每个区域的尺寸仍然大于散焦可忽略区域的尺寸。其次，重新聚焦是通过对已经去啁啾的信号分别以子块的中心进行相位补偿来实现的，每个子块图像由PFA形成。第三，每个子块中超出散焦可忽略区域的区域通过SVPF进行校正，图像失真可以通过二维插值进行校正。最后，应用马赛克图像方法得到高质量的大场景图像。本文提出的算法高效，解决了SAR图像拼接过程中的错位、灰度差异等问题。