一种联合Khatri-Rao子空间与块稀疏压缩感知的差分SAR层析成像方法

王爱春; 向茂生; 汪丙南

doi:10.11999/JEIT160222

一种联合Khatri-Rao子空间与块稀疏压缩感知的差分SAR层析成像方法

doi: 10.11999/JEIT160222

2.
(中国科学院电子学研究所微波成像技术国家级重点实验室北京 100190) ②(中国科学院大学北京 100049) ③(中国资源卫星应用中心北京 100094)

基金项目:

国家发改委卫星及应用产业发展专项项目 (发改委高技[2012]2083号)

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- 被引次数: 0
出版历程
- 收稿日期: 2016-03-07
- 修回日期: 2016-07-18
- 刊出日期: 2017-01-19

Differential SAR Tomography Imaging Based on Khatri-Rao Subspace and Block Compressive Sensing

2.
(National Key Laboratory of Microwave Imaging Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)

Funds:

The National Development and Reform Commission Satellite and Application Development Projects of China [2012] 2083

摘要

摘要: 虽然采用压缩感知技术(Compressive Sensing, CS)的差分SAR层析成像方法实现了4维空间信息的重构，但是此方法仅利用了目标的稀疏特性并没有考虑目标的结构特性，因此对同时具有稀疏特性和结构特性的目标进行重构时其性能较差。针对这一问题，该文采用联合Khatri-Rao子空间和块压缩感知(Khatri-Rao Subspace and Block Compressive Sensing, KRS-BCS)，提出一种差分SAR层析成像方法。该方法依据目标的结构特性和重构观测矩阵具有的Khatri-Rao积性质，将稀疏结构目标的差分SAR层析成像问题转化为Khatri-Rao子空间下的BCS问题，最后对目标进行块稀疏的l1/l2 范数最优化求解。相比CS差分SAR层析成像方法，该方法不仅保持了CS差分SAR层析成像方法的高分辨率特点，而且其重构精度更高性能更优。仿真数据和ENVISAT星载ASAR数据以及地面GPS实测数据的试验结果验证了该方法的有效性。
- 差分SAR层析成像技术 /
- Khatri-Rao子空间 /
- 块压缩感知
Abstract: While the use of differential SAR tomography based on Compressive Sensing (CS) makes it possible to reconstruct the four-dimensional information of an observed scene, the performance of the reconstruction decreases for a sparse and structural observed scene due to ignoring the structural characteristics of the observed scene. To deal with this issue, a method using differential SAR tomography based on Khatri-Rao Subspace and Block Compressive Sensing (KRS-BCS) is proposed. Using the structure information of the observed scene and Khatri-Rao product property of the reconstructed observation matrix, the proposed method changes the reconstruction of the sparse and structural observed scene into a BCS problem under Khatri-Rao Subspace, and then the KRS-BCS problem is efficiently solved with a block sparse l1/l2 norm optimization signal model. Compared with existing CS methods, the proposed KRS-BCS method not only maintains the high resolution characteristics of CS methods, but also has higher reconstruction accuracy and better performance. Simulations, ENVISAT-ASAR data and ground-based GPS data verify the effectiveness of the proposed method.
- Differential SAR tomography imaging /
- Khatri-Rao Subspace (KRS) /
- Block Compressive Sensing (BCS)

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