Identification of linear sampled data systems.

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67Jun_Blackner.pdf (3.63 MB)
Authors
Blackner, Ronald Keith
Subjects
Estimation
Identification
Stochastic
Discrete linear models
Advisors
Titus, Harold
Date of Issue
1967
Date
1967-06
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
A 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.
Type
Thesis
Description
URI
https://hdl.handle.net/10945/43048
Series/Report No
Department
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
46 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
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.
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