Detection of multivariate cyclostationarity

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dc.contributor.author Ramírez García, David
dc.contributor.author Schreier, Peter J.
dc.contributor.author Vía, Javier
dc.contributor.author Santamaría, Ignacio
dc.contributor.author Scharf, Louis L.
dc.date.accessioned 2020-11-24T13:15:10Z
dc.date.available 2020-11-24T13:15:10Z
dc.date.issued 2015-10-15
dc.identifier.bibliographicCitation D. 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.issn 1053-587X
dc.identifier.uri http://hdl.handle.net/10016/31470
dc.description.abstract This 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.
dc.language.iso eng
dc.rights © 2015 IEEE
dc.subject.other Cyclostationarity
dc.subject.other Generalized likelihood ratio test (GLRT)
dc.subject.other Locally most powerful invariant test (LMPIT)
dc.subject.other Toeplitz matrix
dc.subject.other Wijsman's Theory
dc.title Detection of multivariate cyclostationarity
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/TSP.2015.2450201
dc.rights.accessRights openAccess
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 5395
dc.identifier.publicationissue 20
dc.identifier.publicationlastpage 5408
dc.identifier.publicationtitle IEEE Transactions on Signal Processing
dc.identifier.publicationvolume 63
dc.identifier.uxxi AR/0000018813
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