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TECHNICAL BRIEFS

Estimation of Steady-State Optimal Filter Gain From Nonoptimal Kalman Filter Residuals

[+] Author and Article Information
Chung-Wen Chen

Mars Mission Research Center, North Carolina State University, Raleigh, NC 27695-7910

Jen-Kuang Huang

Department of Mechanical Engineering and Mechanics, Old Dominion University, Norfolk, VA 23529-0247

J. Dyn. Sys., Meas., Control 116(3), 550-553 (Sep 01, 1994) (4 pages) doi:10.1115/1.2899251 History: Received May 24, 1991; Revised June 21, 1993; Online March 17, 2008

Abstract

This paper proposes a new algorithm to estimate the optimal steady-state Kalman filter gain of a linear, discrete-time, time-invariant stochastic system from nonoptimal Kalman filter residuals. The system matrices are known, but the covariances of the white process and measurement noises are unknown. The algorithm first derives a moving average (MA) model which relates the optimal and nonoptimal residuals. The MA model is then approximated by inverting a long autoregressive (AR) model. From the MA parameters the Kalman filter gain is calculated. The estimated gain in general is suboptimal due to the approximations involved in the method and a finite number of data. However, the numerical example shows that the estimated gain could be near optimal.

Copyright © 1994 by The American Society of Mechanical Engineers
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