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Identification of linear periodically time-varying (LPTV) systems

dc.contributor.advisorSaadat Mehr, Aryanen_US
dc.contributor.committeeMemberWu, Fang-Xiangen_US
dc.contributor.committeeMemberFaried, Sherif O.en_US
dc.contributor.committeeMemberBurton, Richard T.en_US
dc.creatorYin, Wutaoen_US
dc.date.accessioned2009-09-02T12:54:09Zen_US
dc.date.accessioned2013-01-04T04:56:14Z
dc.date.available2010-09-10T08:00:00Zen_US
dc.date.available2013-01-04T04:56:14Z
dc.date.created2009-08en_US
dc.date.issued2009-08en_US
dc.date.submittedAugust 2009en_US
dc.description.abstractA linear periodically time-varying (LPTV) system is a linear time-varying system with the coefficients changing periodically, which is widely used in control, communications, signal processing, and even circuit modeling. This thesis concentrates on identification of LPTV systems. To this end, the representations of LPTV systems are thoroughly reviewed. Identification methods are developed accordingly. The usefulness of the proposed identification methods is verified by the simulation results. A periodic input signal is applied to a finite impulse response (FIR)-LPTV system and measure the noise-contaminated output. Using such periodic inputs, we show that we can formulate the problem of identification of LPTV systems in the frequency domain. With the help of the discrete Fourier transform (DFT), the identification method reduces to finding the least-squares (LS) solution of a set of linear equations. A sufficient condition for the identifiability of LPTV systems is given, which can be used to find appropriate inputs for the purpose of identification. In the frequency domain, we show that the input and the output can be related by using the discrete Fourier transform (DFT) and a least-squares method can be used to identify the alias components. A lower bound on the mean square error (MSE) of the estimated alias components is given for FIR-LPTV systems. The optimal training signal achieving this lower MSE bound is designed subsequently. The algorithm is extended to the identification of infinite impulse response (IIR)-LPTV systems as well. Simulation results show the accuracy of the estimation and the efficiency of the optimal training signal design.en_US
dc.identifier.urihttp://hdl.handle.net/10388/etd-09022009-125409en_US
dc.language.isoen_USen_US
dc.subjectDiscrete Fourier Transformen_US
dc.subjectPeriodic Inputen_US
dc.subjectMean Square Erroren_US
dc.subjectLinear Periodically Time-Vayring Systemsen_US
dc.subjectLeast-Squaresen_US
dc.titleIdentification of linear periodically time-varying (LPTV) systemsen_US
dc.type.genreThesisen_US
dc.type.materialtexten_US
thesis.degree.departmentElectrical Engineeringen_US
thesis.degree.disciplineElectrical Engineeringen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M.Sc.)en_US

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