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dc.contributor.advisorParker, Sydney R.
dc.contributor.authorKlich, David J.
dc.dateMarch 1982
dc.date.accessioned2012-11-20T00:07:23Z
dc.date.available2012-11-20T00:07:23Z
dc.date.issued1982-03
dc.identifier.urihttp://hdl.handle.net/10945/20144
dc.descriptionApproved for public release; distribution is unlimited
dc.description.abstractThe single channel autoregressive lattice has been successfully applied to problems including speech analysis and recognition, spectral analysis and noise cancelling. More recently the two channel autoregressive (AR) lattice has been exploited for autoregressive moving average (ARMA) analysis of systems for modeling and identification. This dissertation considers the multichannel AR lattice when applied to ARMA systems analysis. Constraints on lattice parameters, based on the input output relations of the system under test, are developed. The lattice is redefined in terms of the frequency domain representation of the input data. This proves to be useful because it allows the input to be normalized so that the lattice yields a consistant set of parameters independent of the test source characteristics. Lastly the lattice is redefined in terms of correlations of the input signals. This results in a computationally and storage efficient lattice algorithm.
dc.description.urihttp://archive.org/details/efficientmultich00klic
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
dc.titleEfficient multichannel autoregressive modeling in time and frequency domain.en_US
dc.typeThesisen_US
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.contributor.departmentElectrical Engineering
dc.subject.authorautoregressiveen_US
dc.subject.authorautoregressive latticeen_US
dc.subject.authorautoregressive moving averageen_US
dc.subject.authorautoregressive moving average latticeen_US
dc.subject.authorautoregressive moving average modelingen_US
dc.subject.authorautoregressive modelingen_US
dc.subject.authorsystem identificationen_US
dc.subject.authorsystem modelingen_US
dc.description.serviceLieutenant Commander, United States Navy
etd.thesisdegree.namePh.D.en_US
etd.thesisdegree.levelDoctoralen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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