于美婷2,
匡纲要1
1.国防科技大学电子信息系统复杂电磁环境效应国家重点实验室 长沙 410073
2.国防科技大学电子导航与时空技术工程研究中心 长沙 410073
基金项目:国家自然科学基金(61701508)，湖南省自然科学基金(2018JJ3613)
详细信息
作者简介:张思乾：女，1987年生，副教授，研究方向为SAR信号处理、稀疏表征
于美婷：女，1988年生，讲师，研究方向为SAR图像处理、稀疏表征
匡纲要：男，1966年生，教授，研究方向为SAR图像处理、信号处理
通讯作者:张思乾 zhangsiqian@nudt.edu.cn
中图分类号:TN959.3计量
文章访问数:373
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被引次数:0
出版历程
收稿日期:2020-04-17
修回日期:2020-11-21
网络出版日期:2020-11-25
刊出日期:2021-06-18
A Three-Dimensional Imaging Algorithm of Downward-looking Sparse Linear Array SAR Based on Low-rank Tensor
Siqian ZHANG1,,,Meiting YU2,
Gangyao KUANG1
1. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China
2. Engineering Research Center for Position, Navigation and Time, National University of Defense Technology, Changsha 410073, China
Funds:The Natural National Science Foundation of China (61701508), The Natural Science Foundation of Hunan Province (2018JJ3613)
摘要
摘要:为了解决3维稀疏数据处理中向量化或矩阵化带来的原始空间结构破坏与计算复杂度高的问题，该文针对下视稀疏线阵3维SAR成像几何模型和回波信号特点，构建了张量空间信号模型，提出了一种基于低秩张量补全的3维SAR稀疏成像算法。该算法首先利用回波张量的低秩性，通过张量补全重构稀疏回波中的丢失元素，再对补全后的全采样信号张量进行3维成像，从而获得高效率、低旁瓣、高分辨率3维图像。基于X波段下视稀疏线阵3维SAR点目标回波进行了3维成像仿真实验，比较了在不同信噪比和采样率条件下的成像性能，并基于实测数据进一步验证了该算法的有效性和优势。
关键词:合成孔径雷达/
3维成像/
下视/
稀疏重构/
张量补全
Abstract:In order to solving the problems of the inner structure damage and the high computation load brought by the vectorizing or matrixing of 3-D sparse data, the 3-D signal model is established in tensor space for downward-looking sparse linear array three-dimensional SAR. Based on this signal model, a three-dimensional SAR sparse imaging algorithm is proposed in this paper. The missing data firstly can be recovered by tensor completion on the assumption that the echo tensor is essentially low rank. Then, the resulting 3-D images can be well focused by any Fourier transform-based 3-D imaging algorithms with the recovered full-sampled data tensor. The proposed algorithm achieves not only high resolution and low-level side-lobes but also the ideal computational cost and memory consumption, which verified by several numerical simulations and multiple comparative studies on real data.
Key words:Synthetic Aperture Radar(SAR)/
3-D imaging/
Downward-looking/
Sparse reconstruction/
Tensor completion
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