A maximum likelihood estimator for linear and nonlinear systems-a practical application of estimation techniques in measurement problems

Author

Schoukens, J. ; Pintelon, Rik ; Renneboog, J.

Author_Institution

Dept. of Electr. Meas., Vrije Univ. Brussel, Belgium

Volume

37

Issue

1

fYear

1988

fDate

3/1/1988 12:00:00 AM

Firstpage

10

Lastpage

17

Abstract

A method is presented for estimating the parameters of linear systems and nonlinear systems. The linear systems are modeled by their transfer function, while the nonlinear systems are described by a Volterra series. The estimator belongs to the class of maximum-likelihood estimators. During the estimation process, the Cramer-Rao lower bound on the covariance matrix of the estimates is derived

Keywords

estimation theory; linear systems; measurement theory; nonlinear systems; parameter estimation; transfer functions; Cramer-Rao lower bound; Volterra series; covariance matrix; estimation techniques; maximum likelihood estimator; measurement theory; nonlinear systems; parameters of linear systems; transfer function; Continuous time systems; Frequency estimation; Least squares approximation; Linear systems; Maximum likelihood estimation; Noise measurement; Parameter estimation; Probability density function; Time measurement; Transfer functions;

fLanguage

English

Journal_Title

Instrumentation and Measurement, IEEE Transactions on

Publisher

ieee

ISSN

0018-9456

Type

jour

DOI

10.1109/19.2655

Filename

2655