Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing

Sensors

Peter Jung¹  Udaya S. K. P. Miriya Thanthrige²  Aydin Sezgin² 

[1] Institute of Communications and Information Theory, Technical University Berlin, 10587 Berlin, Germany;Institute of Digital Communication Systems, Ruhr University Bochum, 44801 Bochum, Germany;

关键词: algorithm unfolding;    clutter suppression;    defects detection;    compressive sensing;    reweighted norm;   

DOI  :  10.3390/s22083065

来源: DOAJ

【 摘 要 】

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 ${\ell }_1$-norm (a double-reweighted approach) to obtain a higher accuracy compared to the conventional nuclear norm and ${\ell }_1$-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.

【 授权许可】

Unknown