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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

王爱春, 向茂生, 汪丙南. 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[J]. 电子与信息学报, 2017, 39(1): 95-102. doi: 10.11999/JEIT160222
引用本文: 王爱春, 向茂生, 汪丙南. 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[J]. 电子与信息学报, 2017, 39(1): 95-102. doi: 10.11999/JEIT160222
WANG Aichun, XIANG Maosheng, WANG Bingnan. Differential SAR Tomography Imaging Based on Khatri-Rao Subspace and Block Compressive Sensing[J]. Journal of Electronics & Information Technology, 2017, 39(1): 95-102. doi: 10.11999/JEIT160222
Citation: WANG Aichun, XIANG Maosheng, WANG Bingnan. Differential SAR Tomography Imaging Based on Khatri-Rao Subspace and Block Compressive Sensing[J]. Journal of Electronics & Information Technology, 2017, 39(1): 95-102. 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
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)

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

Funds: 

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

  • 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。
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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

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