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基于扰动的结合Off-grid目标的层析SAR三维成像方法
引用本文:杜邦,仇晓兰,张柘,雷斌,丁赤飚.基于扰动的结合Off-grid目标的层析SAR三维成像方法[J].雷达学报,2022,11(1):62-70.
作者姓名:杜邦  仇晓兰  张柘  雷斌  丁赤飚
作者单位:1.中国科学院空间信息处理与应用系统技术重点实验室 北京 1001902.中国科学院空天信息创新研究院 北京 1001903.中国科学院大学 北京 1000494.苏州空天信息研究院 苏州 215123
基金项目:国家自然科学基金(61991421% 61991420)
摘    要:层析合成孔径雷达(TomoSAR)通过组合在不同高度上获取的多基线二维SAR数据,实现合成孔径雷达的三维成像。TomoSAR的求解本质是一维谱估计问题,基于压缩感知的方法可以在非均匀分布的少量基线观测下实现求解,逐渐成为了主流的成像方式。在经典的压缩感知算法流程中,需要将连续的高程向划分成固定的网格,并且假定目标正好位于所划分的网格上。然而该假设通常难以成立,从而引起"基失配"问题,目前该问题在TomoSAR中很少被讨论。该文首先讨论了目标不在网格(Off-grid)上的TomoSAR观测模型,提出了采用加性扰动项来修正目标偏离网格所带来影响的求解模型。在此基础之上,引入局部优化算法与L;范数最小化结合的方法,求解所提出的Off-grid TomoSAR模型。最后,利用仿真数据和机载阵列干涉SAR实际飞行数据进行了验证,结果表明,对于Off-grid目标,该方法能够得到比基于L;范数最小化的经典方法更准确的位置、幅度和相位求解结果,证明了方法的优越性。

关 键 词:压缩感知    层析合成孔径雷达    离网格    扰动    局部阈值
收稿时间:2021-07-01

L1 Minimization with Perturbation for Off-grid Tomographic SAR Imaging
DU Bang,QIU Xiaolan,ZHANG Zhe,LEI Bin,DING Chibiao.L1 Minimization with Perturbation for Off-grid Tomographic SAR Imaging[J].Journal of Radars,2022,11(1):62-70.
Authors:DU Bang  QIU Xiaolan  ZHANG Zhe  LEI Bin  DING Chibiao
Affiliation:1.Key Laboratory of Technology in Geospatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China2.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China3.University of Chinese Academy of Sciences, Beijing 100049, China4.Suzhou Aerospace Information Research Institute, Suzhou 215123, China
Abstract:Synthetic Aperture Radar (SAR) Tomography (TomoSAR) is a novel technique that enables three-Dimensional (3-D) imaging using multi-baseline two-Dimensional (2-D) data. The essence of TomoSAR is actually to solve a one-dimensional spectral estimation problem. Compressed Sensing-based (CS) algorithm can retrieve solutions with only a few non-uniform acquisitions and has gradually become the main imaging method. In the conventional processing flow of CS algorithms, the continuous elevation direction is divided into a pre-set grid, and the targets are assumed to be exactly on the grid. \begin{document}$ {{L}}_{1} $\end{document} minimization has been proven to be effective in TomoSAR imaging. In the conventional processing flow, the continuous elevation axis is divided into fixed grids, and scatters are assumed to be exactly on the pre-set grid. However, this hypothesis is generally untenable, and will lead to a problem called “Basis Mismatch”, which is rarely discussed in TomoSAR. In this letter, we first discuss the model of Off-grid TomoSAR, and then propose an addictive perturbation model to compensate for the errors caused by the grid effect. We utilize the local optimization thresholding algorithm to solve the complex-valued \begin{document}$ {{L}}_{1} $\end{document} minimization problem of TomoSAR. We conducted experiments both on simulation data and actual airborne flight data. Our simulation results indicate that the proposed method can estimate a more accurate position of scatters, which leads to better original signal recovery. The reconstruction results of actual data verify that the impact of grid mismatch can be mostly eliminated.
Keywords:Compressive Sensing (CS)  Synthetic Aperture Radar Tomography (TomoSAR)  Off-grid  Perturbation  Locally optimization thresholding
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