Advanced image formation and processing of partial synthetic aperture radar data
Advanced image formation and processing of partial synthetic aperture radar data
复制标题
部分合成孔径雷达数据的高级图像形成和处理
DOI：
10.1049/iet-spr.2011.0073
复制
发表时间：
2012
影响因子：
1.7
通讯作者：
Kelly S
中科院分区：
文献类型：
--
作者：
Kelly S
作者提出了一种基于迭代反演算法的先进合成孔径雷达（SAR）图像形成框架，该算法近似解决了正则化最小二乘问题。与标准方法相比，该框架在某些成像场景中提供了改进的图像重建，例如当SAR数据欠采样时。迭代算法还允许使用先验信息来解决其他问题，例如SAR数据中未知相位误差的校正。然而，迭代反演框架是可行的，快速算法的生成模型及其伴随必须是可用的。作者演示了如何快速，N2 log 2N复杂性，（重/回）-投影算法可以用作生成模型及其伴随的精确近似，而不限制其他N2 log 2N方法的几何近似，例如，极坐标格式算法。实验结果表明，他们的框架使用公开的SAR数据集的有效性。
The authors propose an advanced synthetic aperture radar (SAR) image formation framework based on iterative inversion algorithms that approximately solve a regularised least squares problem. The framework provides improved image reconstructions, compared to the standard methods, in certain imaging scenarios, for example when the SAR data are under-sampled. Iterative algorithms also allow prior information to be used to solve additional problems such as the correction of unknown phase errors in the SAR data. However, for an iterative inversion framework to be feasible, fast algorithms for the generative model and its adjoint must be available. The authors demonstrate how fast,N2log2Ncomplexity, (re/back)-projection algorithms can be used as accurate approximations for the generative model and its adjoint, without the limiting geometric approximations of otherN2log2Nmethods, for example, the polar format algorithm. Experimental results demonstrate the effectiveness of their framework using publicly available SAR datasets.
影响因子：
1.3
作者：
A. Rogan;R. Carande
通讯作者：
R. Carande