论文标题
基于加权低级别以及稀疏恢复的MIMO无线传感的缺陷检测
Defect Detection by MIMO Wireless Sensing based on Weighted Low-Rank plus Sparse Recovery
论文作者
论文摘要
我们通过多个输入多重输出（MIMO）无线雷达提出了基于压缩感应的缺陷检测。在这里，缺陷在分层的材料结构内部，因此，由于从分层材料结构的表面进行反射，缺陷检测具有挑战性。通过利用分层材料结构的反射的低级别性质和缺陷的稀疏性质，我们提出了一种基于等级最小化和稀疏恢复的方法。为了提高低级别和稀疏组件的恢复准确性，我们提出了一种基于迭代重新持续核定标准的非凸方法，并迭代地重新持续了$ \ ell_1- $ norm算法。我们的数值结果表明，所提出的方法能够将缺陷和分层结构的信号响应从大幅减少的观测值中获取成功。此外，拟议的方法的表现优于最先进的杂物方法
We present a compressive sensing based defect detection by multiple input multiple output (MIMO) wireless radar. Here, defects are inside a layered material structure, therefore, due to reflections from the surface of the layered material structure the defect detection is challenging. By utilizing a low-rank nature of the reflections of the layered material structure and sparse nature of the defects, we propose a method based on rank minimization and sparse recovery. To improve the accuracy in the recovery of low-rank and sparse components, we propose a non-convex approach based on the iteratively reweighted nuclear norm and iteratively reweighted $\ell_1-$norm algorithm. Our numerical results show that the proposed method is able to demix and recover the signalling responses of the defects and layered structure successfully from substantially reduced number of observations. Further, the proposed approach outperforms the state-of-the-art clutter reduction approaches
