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Monterey, California. Naval Postgraduate School
Identification of linear sampled data systems.
Abstract
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.
Degree
thesis:*- Grantor dc:publisher
- Monterey, California. Naval Postgraduate School
- Year dc:date.issued
- 1967
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Blackner, Ronald Keith
- Advisor dc:contributor.advisor
-
- Titus, Harold
Rights
dc:rights- Statement dc:rights
-
- This 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.
- Language dc:language.iso
- en_US
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- https://hdl.handle.net/10945/43048
- OAI identifier oai:identifier
- oai:calhoun.nps.edu:10945/43048