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Least-Mean-Square Receding Horizon Estimation
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-03-05
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Abstract EN
We propose a least-mean-square (LMS) receding horizon (RH) estimator for state estimation.
The proposed LMS RH estimator is obtained from the conditional expectation of the estimated state given a finite number of inputs and outputs over the recent finite horizon.
Any a priori state information is not required, and existing artificial constraints for easy derivation are not imposed.
For a general stochastic discrete-time state space model with both system and measurement noise, the LMS RH estimator is explicitly represented in a closed form.
For numerical reliability, the iterative form is presented with forward and backward computations.
It is shown through a numerical example that the proposed LMS RH estimator has better robust performance than conventional Kalman estimators when uncertainties exist.
American Psychological Association (APA)
Kwon, Bokyu& Han, Soohee. 2012. Least-Mean-Square Receding Horizon Estimation. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-19.
https://search.emarefa.net/detail/BIM-1001785
Modern Language Association (MLA)
Kwon, Bokyu& Han, Soohee. Least-Mean-Square Receding Horizon Estimation. Mathematical Problems in Engineering No. 2012 (2012), pp.1-19.
https://search.emarefa.net/detail/BIM-1001785
American Medical Association (AMA)
Kwon, Bokyu& Han, Soohee. Least-Mean-Square Receding Horizon Estimation. Mathematical Problems in Engineering. 2012. Vol. 2012, no. 2012, pp.1-19.
https://search.emarefa.net/detail/BIM-1001785
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1001785