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Receding Horizon Least Squares Estimator with Application to Estimation of Process and Measurement Noise Covariances
Joint Authors
Shin, Vladimir
Kim, Yoonsoo
Thien, Rebbecca T. Y.
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-19
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
This paper presents a noise covariance estimation method for dynamical models with rectangular noise gain matrices.
A novel receding horizon least squares criterion to achieve high estimation accuracy and stability under environmental uncertainties and experimental errors is proposed.
The solution to the optimization problem for the proposed criterion gives equations for a novel covariance estimator.
The estimator uses a set of recent information with appropriately chosen horizon conditions.
Of special interest is a constant rectangular noise gain matrices for which the key theoretical results are obtained.
They include derivation of a recursive form for the receding horizon covariance estimator and iteration procedure for selection of the best horizon length.
Efficiency of the covariance estimator is demonstrated through its implementation and performance on dynamical systems with an arbitrary number of process and measurement noises.
American Psychological Association (APA)
Shin, Vladimir& Thien, Rebbecca T. Y.& Kim, Yoonsoo. 2018. Receding Horizon Least Squares Estimator with Application to Estimation of Process and Measurement Noise Covariances. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1207901
Modern Language Association (MLA)
Shin, Vladimir…[et al.]. Receding Horizon Least Squares Estimator with Application to Estimation of Process and Measurement Noise Covariances. Mathematical Problems in Engineering No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1207901
American Medical Association (AMA)
Shin, Vladimir& Thien, Rebbecca T. Y.& Kim, Yoonsoo. Receding Horizon Least Squares Estimator with Application to Estimation of Process and Measurement Noise Covariances. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1207901
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1207901