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dc.contributor.authorRamírez García, David
dc.contributor.authorSchreier, Peter J.
dc.contributor.authorVía Rodríguez, Javier 
dc.contributor.authorSantamaría Caballero, Luis Ignacio 
dc.contributor.authorScharf, Louis L. 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2016-11-09T17:10:02Z
dc.date.available2016-11-09T17:10:02Z
dc.date.issued2015-10
dc.identifier.issn1053-587X
dc.identifier.issn1941-0476
dc.identifier.otherTEC2013-47141-C4-3-Res_ES
dc.identifier.urihttp://hdl.handle.net/10902/9512
dc.description.abstractThis paper derives an asymptotic generalized likelihood ratio test (GLRT) and an asymptotic locally most powerful invariant test (LMPIT) for two hypothesis testing problems: 1) Is a vector-valued random process cyclostationary (CS) or is it wide-sense stationary (WSS)? 2) Is a vector-valued random process CS or is it nonstationary? Our approach uses the relationship between a scalar-valued CS time series and a vector-valued WSS time series for which the knowledge of the cycle period is required. This relationship allows us to formulate the problem as a test for the covariance structure of the observations. The covariance matrix of the observations has a block-Toeplitz structure for CS and WSS processes. By considering the asymptotic case where the covariance matrix becomes block-circulant we are able to derive its maximum likelihood (ML) estimate and thus an asymptotic GLRT. Moreover, using Wijsman's theorem, we also obtain an asymptotic LMPIT. These detectors may be expressed in terms of the Loe`ve spectrum, the cyclic spectrum, and the power spectral density, establishing how to fuse the information in these spectra for an asymptotic GLRT and LMPIT. This goes beyond the state-of-the-art, where it is common practice to build detectors of cyclostationarity from ad-hoc functions of these spectra.es_ES
dc.description.sponsorshipThe work of P. Schreier was supported by the Alfried Krupp von Bohlen und Halbach Foundation, under its program “Return of German scientists from abroad”. The work of I. Santamaría and J. Vía was supported by the Spanish Government, Ministerio de Ciencia e Innovación (MICINN), under project RACHEL (TEC2013-47141-C4-3-R). The work of L. Scharf was supported by the Airforce Office of Scientific Research under contract FA9550-10-1-0241.es_ES
dc.format.extent13 p.es_ES
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es_ES
dc.rights© 2015 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, 2015, 63(20), 5395 - 5408es_ES
dc.subject.otherCyclostationarityes_ES
dc.subject.otherGeneralized likelihood ratio test (GLRT)es_ES
dc.subject.otherLocally most powerful invariant test (LMPIT)es_ES
dc.subject.otherToeplitz matrixes_ES
dc.subject.otherWijsman’s theoremes_ES
dc.titleDetection of multivariate cyclostationarityes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1109/TSP.2015.2450201es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1109/TSP.2015.2450201
dc.type.versionacceptedVersiones_ES


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