论文标题
使用张量方法的合成孔径成像和运动估算
Synthetic aperture imaging and motion estimation using tensor methods
论文作者
论文摘要
我们考虑一种合成的孔径成像构型，例如合成孔径雷达（SAR），我们想首先要将反射与移动目标与来自固定背景的目标分开，然后将其分别映像为移动和固定反射器。为此，我们将数据表示为三阶张量，该张量是由来自部分重叠的子量的数据形成的。然后，我们将张量强的主组件分析（TRPCA）应用于张量数据，该数据将它们分为来自固定和移动反射器的部分。与分离的数据集形成图像。我们的分析表明，与通常的矩阵案例相比，TRPCA的性能明显提高。特别是，张量分解可以识别使用常规运动估计方法（包括矩阵RPCA）时无法检测到的运动特征。我们用X频段雷达制度中的数值模拟说明了该方法的性能。
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 sub-apertures. We then apply a tensor robust principal component analysis (TRPCA) to the tensor data which separates them into the parts coming from the stationary and moving reflectors. Images are 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.
