A polynomial algorithm for noise identification in linear systems

Author

Morein, Robert T. ; Kalata, Paul

Author_Institution

Automata Design Assoc., Dresher, PA, USA

fYear

1990

fDate

5-7 Sep 1990

Firstpage

595

Abstract

The implementation of an optimal adaptive filter in a composite system consisting of a Kalman filter and a noise covariance identifier is addressed. A method of noise identification employing a polynomial transformation of the system output is presented. The method eliminates batch processing, indirect observation through filtering apparatus, and the need for a priori estimates for tuning the noise estimation apparatus. The result is a robust method, suitable for online incremental update, and time-varying systems, free of tunable parameters or a priori assumptions other than the values of the deterministic system parameters, and highly parallelizable

Keywords

Kalman filters; adaptive control; adaptive filters; linear systems; noise; tuning; Kalman filter; composite system; deterministic system parameters; linear systems; noise covariance identifier; noise identification; online incremental update; optimal adaptive filter; polynomial algorithm; time-varying systems; tunable parameters; tuning; Equations; Information filtering; Information filters; Kalman filters; Linear systems; Noise measurement; Polynomials; Q measurement; State estimation; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control, 1990. Proceedings., 5th IEEE International Symposium on

Conference_Location

Philadelphia, PA

ISSN

2158-9860

Print_ISBN

0-8186-2108-7

Type

conf

DOI

10.1109/ISIC.1990.128518

Filename

128518