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dc.contributor.advisorTitus, Harold
dc.contributor.authorBlackner, Ronald Keith
dc.date1967-06
dc.date.accessioned2014-08-15T15:15:42Z
dc.date.available2014-08-15T15:15:42Z
dc.date.issued1967
dc.identifier.urihttp://hdl.handle.net/10945/43048
dc.descriptionApproved for public release, distribution unlimiteden_US
dc.description.abstractA least squares estimator is derived for the state transition matrix phi of a linear, stationary sampled data system operating in a stochastic environment. The estimator is shown to be unbiased and minimum variance under the condition of full observability of the state vector of the system. The estimator is also shown to be the Maximum Likelihood Estimator for the case of the stochastic environment having Gaussian statistics. The estimation scheme is compared with two other recently published estimation schemes, both of which are shown to be special cases of the scheme herein presented.en_US
dc.description.urihttp://archive.org/details/identificationof1094543048
dc.format.extent46 p.en_US
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.subject.lcshElectronicsen_US
dc.titleIdentification of linear sampled data systems.en_US
dc.typeThesisen_US
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.subject.authorEstimationen_US
dc.subject.authorIdentificationen_US
dc.subject.authorStochasticen_US
dc.subject.authorDiscrete linear modelsen_US
dc.description.serviceU.S. Navy (USN) authoren_US
dc.identifier.oclcocn640327361
etd.thesisdegree.nameM.S. in Engineering Electronicsen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineEngineering Electronicsen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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