Institute of Digital Communication Systems, Ruhr University Bochum, 44801 Bochum, Germany.
Institute of Communications and Information Theory, Technical University Berlin, 10587 Berlin, Germany.
Sensors (Basel). 2022 Apr 15;22(8):3065. doi: 10.3390/s22083065.
We address the detection of material defects, which are inside a layered material structure using compressive sensing-based multiple-input and multiple-output (MIMO) wireless radar. Here, strong clutter due to the reflection of the layered structure's surface often makes the detection of the defects challenging. Thus, sophisticated signal separation methods are required for improved defect detection. In many scenarios, the number of defects that we are interested in is limited, and the signaling response of the layered structure can be modeled as a low-rank structure. Therefore, we propose joint rank and sparsity minimization for defect detection. In particular, we propose a non-convex approach based on the iteratively reweighted nuclear and ℓ1-norm (a double-reweighted approach) to obtain a higher accuracy compared to the conventional nuclear norm and ℓ1-norm minimization. To this end, an iterative algorithm is designed to estimate the low-rank and sparse contributions. Further, we propose deep learning-based parameter tuning of the algorithm (i.e., algorithm unfolding) to improve the accuracy and the speed of convergence of the algorithm. Our numerical results show that the proposed approach outperforms the conventional approaches in terms of mean squared errors of the recovered low-rank and sparse components and the speed of convergence.
我们使用基于压缩感知的多输入多输出（MIMO）无线雷达来解决分层材料结构内部的材料缺陷检测问题。这里，由于分层结构表面的反射产生的强烈杂波，使得缺陷检测具有挑战性。因此，需要复杂的信号分离方法来提高缺陷检测的性能。在许多场景中，我们感兴趣的缺陷数量是有限的，并且分层结构的信号响应可以建模为低秩结构。因此，我们提出了联合秩和稀疏性最小化来进行缺陷检测。具体来说，我们提出了一种基于迭代重加权核范数和 l1 范数（双重加权方法）的非凸方法，与传统的核范数和 l1 范数最小化方法相比，可以获得更高的准确性。为此，设计了一种迭代算法来估计低秩和稀疏贡献。此外，我们提出了基于深度学习的算法参数调整（即算法展开），以提高算法的准确性和收敛速度。我们的数值结果表明，与传统方法相比，所提出的方法在恢复的低秩和稀疏分量的均方误差和收敛速度方面表现更好。
