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Synthetic Aperture Imaging and Motion Estimation Using Tensor Methods
SIAM Journal on Imaging Sciences ( IF 2.1 ) Pub Date : 2020-12-15 , DOI: 10.1137/19m1306440
Matan Leibovich , George Papanicolaou , Chrysoula Tsogka

SIAM Journal on Imaging Sciences, Volume 13, Issue 4, Page 2213-2249, January 2020.
We consider a synthetic aperture imaging configuration, such as synthetic aperture radar (SAR), where we want to first separate reflections from moving targets from those coming from a stationary background, and then to image separately the moving and the stationary reflectors. For this purpose, we introduce a representation of the data as a third-order tensor formed from data coming from partially overlapping subapertures. We then apply a tensor robust principal component analysis (TRPCA) to the tensor data which separates it into the parts coming from the stationary and moving reflectors. A key feature of the proposed algorithm is the use of a Fourier-based tensor nuclear norm which is well adapted to the SAR data structure. Images are then formed with the separated data sets. Our analysis shows a distinctly improved performance of TRPCA, compared to the usual matrix case. In particular, the tensor decomposition can identify motion features that are undetectable when using the conventional motion estimation methods, including matrix RPCA. We illustrate the performance of the method with numerical simulations in the X-band radar regime.


中文翻译:

张量法合成孔径成像与运动估计

SIAM影像科学杂志,第13卷,第4期,第2213-2249页,2020年1月。
我们考虑一种合成孔径成像配置,例如合成孔径雷达(SAR),我们希望首先将来自移动目标的反射与来自固定背景的反射分开,然后分别对移动和固定反射器成像。为此,我们将数据表示为由来自部分重叠子孔径的数据形成的三阶张量。然后,我们对张量数据应用张量鲁棒主成分分析(TRPCA),将其分为固定反射器和移动反射器两部分。所提出算法的关键特征是使用基于傅里叶的张量核范数,该张数很适合SAR数据结构。然后用分离的数据集形成图像。我们的分析表明TRPCA的性能有了明显改善,与通常的矩阵情况相比。特别地,张量分解可以识别当使用包括矩阵RPCA的常规运动估计方法时不可检测的运动特征。我们在X波段雷达方案中通过数值模拟说明了该方法的性能。
更新日期:2020-12-16
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