Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-15642
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Main Title: Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing
Author(s): Miriya Thanthrige, Udaya S. K. P.
Jung, Peter
Sezgin, Aydin
Type: Article
URI: https://depositonce.tu-berlin.de/handle/11303/16864
http://dx.doi.org/10.14279/depositonce-15642
License: https://creativecommons.org/licenses/by/4.0/
Abstract: 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.
Subject(s): algorithm unfolding
clutter suppression
defects detection
compressive sensing
reweighted norm
Issue Date: 15-Apr-2022
Date Available: 10-May-2022
Language Code: en
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Sponsor/Funder: DFG, 287022738, TRR 196: Mobile Material-Charakterisierung und -Ortung durch Elektromagnetische Abtastung
BMBF, 01DD20001, Künstliche Intelligenz in der Erdbeobachtung: Schlussfolgern, Unsicherheiten, Ethik und darüber hinaus
Journal Title: Sensors
Publisher: MDPI
Volume: 22
Issue: 8
Article Number: 3065
Publisher DOI: 10.3390/s22083065
EISSN: 1424-8220
TU Affiliation(s): Fak. 4 Elektrotechnik und Informatik » Inst. Telekommunikationssysteme » FG Theoretische Grundlagen der Kommunikationstechnik
Appears in Collections:Technische Universität Berlin » Publications

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