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dc.contributor.advisorHamming, Richard W.
dc.contributor.authorCostello, Walter Jeremiah
dc.dateMarch 1980
dc.date.accessioned2012-11-16T18:30:42Z
dc.date.available2012-11-16T18:30:42Z
dc.date.issued1980-03
dc.identifier.urihttp://hdl.handle.net/10945/17598
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractThe linear discrete Kalman filter was analyzed using a frequency-domain approach. Process and measurement noise covariances are shown to be critical design parameters which, together with the assumed prior state and covariance estimates, completely determine the main schedule of the linear Kalman filter. Several relevant design techniques are illustrated and discussed. The concepts of smoothing and sharpening are demonstrated. Extensions to adaptive, nonlinear, and non-parametric filtering are briefly discussed, as are applications to inventory management, estimation of time-varying mean functions, and multiple regression.en_US
dc.description.urihttp://archive.org/details/frequencydomaina00cost
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. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.en_US
dc.titleA frequency domain analysis of the linear discrete Kalman filteren_US
dc.typeThesisen_US
dc.contributor.secondreaderGaver, Donald P.
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.contributor.departmentDepartment of Operations Research
dc.subject.authorKalman filteren_US
dc.subject.authorDigital filtersen_US
dc.subject.authorSmoothingen_US
dc.description.serviceLieutenant Colonel, United States Marine Corpsen_US
etd.thesisdegree.nameM.S. in Operations Researchen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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