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

发布时间：2018-04-21 08:02

本文选题：差分SAR层析成像技术 + Khatri-Rao子空间　；参考：《电子与信息学报》2017年01期

【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l_1/l_2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。
[Abstract]:Although the Compressive Sensing (CS) differential SAR tomography method realizes the reconstruction of 4 dimensional spatial information, this method uses only the sparse feature of the target and does not take into account the structural characteristics of the target. Therefore, the performance of the target is poor when the target is reconstructed with both the sparse and the structure characteristics. This problem, using the joint Khatri-Rao subspace and the block compression perception (Khatri-Rao Subspace and Block Compressive Sensing, KRS-BCS), presents a differential SAR tomography method. This method is based on the structure properties of the target and the Khatri-Rao product properties of the reconstruction of the observation matrix, and the difference SAR tomography of the sparse structure target is made. The problem is transformed into the BCS problem under the Khatri-Rao subspace. Finally, the target is optimized by the block sparse l_1/l_2 norm. Compared with the CS differential SAR tomography, this method not only maintains the high resolution characteristic of the CS differential SAR tomography method, but also has better performance of the reconstruction precision. The simulation data and the ENVISAT star borne ASAR number are better. The effectiveness of the method is verified by the experimental results of ground GPS data.

【作者单位】：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;中国科学院大学;中国资源卫星应用中心;
【基金】：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)~~
【分类号】：TN957.52

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