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dc.contributor.authorRamírez García, David
dc.contributor.authorRomero, Daniel
dc.contributor.authorVía Rodríguez, Javier 
dc.contributor.authorLópez Valcarce, Roberto
dc.contributor.authorSantamaría Caballero, Luis Ignacio 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2018-12-18T14:47:43Z
dc.date.available2018-12-18T14:47:43Z
dc.date.issued2018-12-01
dc.identifier.issn1053-587X
dc.identifier.issn1941-0476
dc.identifier.otherTEC2015-69648-REDCes_ES
dc.identifier.otherTEC2016-81900-REDT/AEIes_ES
dc.identifier.otherTEC2015-69868-C2-1-Res_ES
dc.identifier.otherTEC2016-76409-C2-2-Res_ES
dc.identifier.otherTEC2016-75067-C4-4-Res_ES
dc.identifier.otherTEC2017-86921-C2-1-Res_ES
dc.identifier.otherTEC2017-86921-C2-2-Res_ES
dc.identifier.urihttp://hdl.handle.net/10902/15182
dc.description.abstractThis paper studies the existence of optimal invariant detectors for determining whether P multivariate processes have the same power spectral density. This problem finds application in multiple fields, including physical layer security and cognitive radio. For Gaussian observations, we prove that the optimal invariant detector, i.e., the uniformly most powerful invariant test, does not exist. Additionally, we consider the challenging case of close hypotheses, where we study the existence of the locally most powerful invariant test (LMPIT). The LMPIT is obtained in the closed form only for univariate signals. In the multivariate case, it is shown that the LMPIT does not exist. However, the corresponding proof naturally suggests an LMPIT-inspired detector that outperforms previously proposed detectors.es_ES
dc.description.sponsorshipThis work was partly supported by the Spanish MINECO grants COMONSENS Network (TEC2015-69648-REDC) and KERMES Network (TEC2016-81900-REDT/AEI); by the Spanish MINECO and the European Commission (ERDF) grants ADVENTURE (TEC2015-69868-C2-1- R), WINTER (TEC2016-76409-C2-2-R), CARMEN (TEC2016-75067-C4-4- R) and CAIMAN (TEC2017-86921-C2-1-R and TEC2017-86921-C2-2-R); by the Comunidad de Madrid grant CASI-CAM-CM (S2013/ICE-2845); by the Xunta de Galicia and ERDF grants GRC2013/009, R2014/037 and ED431G/04 (Agrupacion Estratexica Consolidada de Galicia accred- ´ itation 2016-2019); by the SODERCAN and ERDF grant CAIMAN (12.JU01.64661); and by the Research Council of Norway grant FRIPRO TOPPFORSK (250910/F20). This paper was presented in part at the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing.es_ES
dc.format.extent13 p.es_ES
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es_ES
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.es_ES
dc.sourceIEEE Transactions on Signal Processing, 2018, 66(23), 6268-6280es_ES
dc.subject.otherGeneralized likelihood ratio test (GLRT)es_ES
dc.subject.otherLocally most powerful invariant test (LMPIT)es_ES
dc.subject.otherPower spectral density (PSD)es_ES
dc.subject.otherToeplitz matrixes_ES
dc.subject.otherUniformly most powerful invariant test (UMPIT)es_ES
dc.titleTesting equality of multiple power spectral density matriceses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1109/TSP.2018.2875884es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1109/TSP.2018.2875884
dc.type.versionacceptedVersiones_ES


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