Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing

dc.contributor.authorMiriya Thanthrige, Udaya S. K. P.
dc.contributor.authorJung, Peter
dc.contributor.authorSezgin, Aydin
dc.date.accessioned2022-05-10T13:55:04Z
dc.date.available2022-05-10T13:55:04Z
dc.date.issued2022-04-15
dc.date.updated2022-05-05T14:06:57Z
dc.description.abstractWe 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.en
dc.description.sponsorshipDFG, 287022738, TRR 196: Mobile Material-Charakterisierung und -Ortung durch Elektromagnetische Abtastungen
dc.description.sponsorshipBMBF, 01DD20001, Künstliche Intelligenz in der Erdbeobachtung: Schlussfolgern, Unsicherheiten, Ethik und darüber hinausen
dc.identifier.eissn1424-8220
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/16864
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-15642
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otheralgorithm unfoldingen
dc.subject.otherclutter suppressionen
dc.subject.otherdefects detectionen
dc.subject.othercompressive sensingen
dc.subject.otherreweighted normen
dc.titleDeep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensingen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber3065en
dcterms.bibliographicCitation.doi10.3390/s22083065en
dcterms.bibliographicCitation.issue8en
dcterms.bibliographicCitation.journaltitleSensorsen
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume22en
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Telekommunikationssysteme::FG Theoretische Grundlagen der Kommunikationstechnikde
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Theoretische Grundlagen der Kommunikationstechnikde
tub.affiliation.instituteInst. Telekommunikationssystemede
tub.publisher.universityorinstitutionTechnische Universität Berlinen

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