Publication:
Quantitative signal subspace imaging

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dc.affiliation.dptoUC3M. Departamento de Matemáticases
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Métodos Numéricos y Aplicacioneses
dc.affiliation.institutoUC3M. Instituto Universitario sobre Modelización y Simulación en Fluidodinámica, Nanociencia y Matemática Industrial Gregorio Millán Barbanyes
dc.contributor.authorGonzález Rodríguez, Pedro
dc.contributor.authorKim, Arnold D
dc.contributor.authorTsogka, Chrysoula
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2023-04-26T10:11:00Z
dc.date.available2023-04-26T10:11:00Z
dc.date.issued2021-12
dc.descriptionCorrigendum: Quantitative signal subspace imaging (2021 Inverse Problems 37 125006). Inverse Problems, 38(4), 049501. https://doi.org/10.1088/1361-6420/ac509een
dc.descriptionIn the numerical results of [1], the reported signal-to-noise ratios (SNRs) are incorrect. We give the correct SNR values below. For the results shown in figure 2 and corresponding discussion, SNR = 45.334 dB. For the results shown in figure 7 and corresponding discussion, SNR = 55.146 dB. For the results shown in figure 9 and corresponding discussion, SNR = 45.387 dB. For the results shown in figure 10 and corresponding discussion, SNR = 105.172 dB. For the results shown in figure 11 and corresponding discussion, SNR = 105.172 dB for figure 11(b), SNR = 55.247 dB for figure 11(c), and SNR = 25.211 dB for figure 11(d).en
dc.description.abstractWe develop and analyze a quantitative signal subspace imaging method for single-frequency array imaging. This method is an extension to multiple signal classification which uses (i) the noise subspace to determine the location and support of targets, and (ii) the signal subspace to recover quantitative information about the targets. For point targets, we are able to recover the complex reflectivity and for an extended target under the Born approximation, we are able to recover a scalar quantity that is related to the product of the volume and relative dielectric permittivity of the target. Our resolution analysis for a point target demonstrates this method is capable of achieving exact recovery of the complex reflectivity at subwavelength resolution. Additionally, this resolution analysis shows that noise in the data effectively acts as a regularization to the imaging functional resulting in a method that is surprisingly more robust and effective with noise than without noise.en
dc.description.sponsorshipA D Kim and C Tsogka are supported by the Air Force Office of Scientific Research (FA9550-21-1-0196). A D Kim is also supported by the National Science Foundation (DMS-1840265). Pedro González-Rodríguez is supported by the Spanish Ministerio de Ciencia e Innovación (PID2020-115088RB-100).en
dc.format.extent23
dc.identifier.bibliographicCitationGonzález-Rodríguez, P., Kim, A. D., & Tsogka, C. (2021). Quantitative signal subspace imaging. Inverse Problems, 37(12), 125006.en
dc.identifier.doihttps://doi.org/10.1088/1361-6420/ac349b
dc.identifier.issn0266-5611
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue12, 125006
dc.identifier.publicationlastpage23
dc.identifier.publicationtitleInverse Problemsen
dc.identifier.publicationvolume37
dc.identifier.urihttps://hdl.handle.net/10016/37204
dc.identifier.uxxiAR/0000028504
dc.identifier.uxxiAR/0000031280
dc.language.isoeng
dc.publisherIOP Scienceen
dc.relation.ispartofhttps://doi.org/10.1088/1361-6420/ac509e
dc.relation.projectIDGobierno de España. PID2020-115088RB-I00es
dc.rights© 2021 The Author(s).en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaMatemáticases
dc.subject.ecienciaÓpticaes
dc.subject.otherMultiple signal classificationen
dc.subject.otherLinear samplingen
dc.subject.otherFactorization methoden
dc.subject.otherQuantitative array imagingen
dc.titleQuantitative signal subspace imagingen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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