Improvement of Observer/Kalman Filter Identification (OKID) by Residual Whitening

[+] Author and Article Information
M. Phan

Princeton University, Princeton, NJ 08544

L. G. Horta, J.-N. Juang

NASA Langley Research Center, Hampton, VA 23665

R. W. Longman

Columbia University, New York, NY 10027

J. Vib. Acoust 117(2), 232-239 (Apr 01, 1995) (8 pages) doi:10.1115/1.2873927 History: Received August 01, 1992; Revised July 01, 1993; Online February 26, 2008


This paper presents a time-domain method to identify a state space model of a linear system and its corresponding observer/Kalman filter from a given set of general input-output data. The identified filter has the properties that its residual is minimized in the least squares sense, orthogonal to the time-shifted versions of itself, and to the given input-output data sequences. The connection between the state space model and a particular auto-regressive moving average description of a linear system is made in terms of the Kalman filter and a deadbeat gain matrix. The procedure first identifies the Markov parameters of an observer system, from which a state space model of the system and the filter gain are computed. The developed procedure is shown to improve results obtained by an existing observer/Kalman filter identification method, which is based on an auto-regressive model without the moving average terms. Numerical and experimental results are presented to illustrate the proposed method.

Copyright © 1995 by The American Society of Mechanical Engineers
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