Receding horizon least squares estimator with application to estimation of process and measurement noise covariances
type: Item
instance of: scholarly article
title: Receding horizon least squares estimator with application to estimation of process and measurement noise covariances
zbMATH Open document ID: 1427.93238
DOI: 10.1155/2018/5303694
author: V. I. Shin
author: Rebbecca T. Y. Thien
author: Yoonsoo Kim
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 62H12
zbMATH DE Number: 7019113
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/5303694
OpenAlex ID: W2901089860
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description: scientific article; zbMATH DE number 7019113