Nonparametric identification of linear (almost) periodically time-varying systems using cyclic-polyspectra

A. V. Dandawate, G. B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Novel algorithms are presented for nonparametric input/output identification of systems using kth-order cyclic-polyspectra at known cycles. Errors-in-variables models with generally cyclostationary inputs are considered. The proposed methods for k>or=3 are insensitive to contamination of both input and output data by even cyclostationary Gaussian noise of unknown covariance. Additional insensitivity to different types of input disturbances is delineated. Consistent and asymptotically normal sample cyclic-polyspectrum estimators are used for implementation, and simulations illustrate the proposed algorithms.

Original languageEnglish (US)
Title of host publication1992 IEEE 6th SP Workshop on Statistical Signal and Array Processing, SSAP 1992 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages152-155
Number of pages4
ISBN (Electronic)0780305086, 9780780305083
DOIs
StatePublished - Jan 1 1992
Externally publishedYes
Event6th IEEE SP Workshop on Statistical Signal and Array Processing, SSAP 1992 - Victoria, Canada
Duration: Oct 7 1992Oct 9 1992

Publication series

Name1992 IEEE 6th SP Workshop on Statistical Signal and Array Processing, SSAP 1992 - Conference Proceedings

Conference

Conference6th IEEE SP Workshop on Statistical Signal and Array Processing, SSAP 1992
Country/TerritoryCanada
CityVictoria
Period10/7/9210/9/92

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