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Detection of multivariate cyclostationarity

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA)es
dc.contributor.authorRamírez García, David
dc.contributor.authorSchreier, Peter J.
dc.contributor.authorVía, Javier
dc.contributor.authorSantamaría, Ignacio
dc.contributor.authorScharf, Louis L.
dc.date.accessioned2020-11-24T13:15:10Z
dc.date.available2020-11-24T13:15:10Z
dc.date.issued2015-10-15
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 Loève 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.en
dc.identifier.bibliographicCitationD. Ramírez, P. J. Schreier, J. Vía, I. Santamaría and L. L. Scharf, "Detection of Multivariate Cyclostationarity," in IEEE Transactions on Signal Processing, vol. 63, no. 20, pp. 5395-5408, Oct.15, 2015
dc.identifier.doihttps://doi.org/10.1109/TSP.2015.2450201
dc.identifier.issn1053-587X
dc.identifier.publicationfirstpage5395
dc.identifier.publicationissue20
dc.identifier.publicationlastpage5408
dc.identifier.publicationtitleIEEE Transactions on Signal Processing
dc.identifier.publicationvolume63
dc.identifier.urihttps://hdl.handle.net/10016/31470
dc.identifier.uxxiAR/0000018813
dc.language.isoeng
dc.rights© 2015 IEEE
dc.rights.accessRightsopen access
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherCyclostationarityen
dc.subject.otherGeneralized likelihood ratio test (GLRT)en
dc.subject.otherLocally most powerful invariant test (LMPIT)en
dc.subject.otherToeplitz matrixen
dc.subject.otherWijsman's Theoryen
dc.titleDetection of multivariate cyclostationarityen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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