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New Algorithm for Recursive Estimation in Linear Discrete-Time Systems with Unknown Parameters  

Shin Vladimir (Department of Mechatronics, Gwangju Institute of Science and Technology)
Ahn Jun-Il (Department of Mechatronics, Gwangju Institute of Science and Technology)
Kim Du-Yong (Department of Mechatronics, Gwangju Institute of Science and Technology)
Publication Information
International Journal of Control, Automation, and Systems / v.4, no.4, 2006 , pp. 456-465 More about this Journal
Abstract
The problem of recursive filtering far linear discrete-time systems with uncertainties is considered. A new suboptimal filtering algorithm is herein proposed. It is based on the fusion formula, which represents an optimal mean-square linear combination of local Kalman estimates with weights depending on cross-covariances between local filtering errors. In contrast to the optimal weights, the suboptimal weights do not depend on current measurements, and thus the proposed algorithm can easily be implemented in real-time. High accuracy and efficiency of the suboptimal filtering algorithm are demonstrated on the following examples: damper harmonic oscillator motion and vehicle motion constrained to a plane.
Keywords
Adaptive filtering; discrete-time system; Kalman filtering; mean-square error; partitioning approach;
Citations & Related Records

Times Cited By Web Of Science : 3  (Related Records In Web of Science)
Times Cited By SCOPUS : 3
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