Multiple adaptive fading Schmidt-Kalman filter for unknown bias
type: Item
instance of: scholarly article
title: Multiple adaptive fading Schmidt-Kalman filter for unknown bias
zbMATH Open document ID: 1407.93390
DOI: 10.1155/2014/623930
author: Tai-Shan Lou
author: Meng-Li Xiao
author: Huimin Fu
author: Zhihua Wang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: Unknown biases in dynamic and measurement models of the dynamic systems can bring greatly negative effects to the state estimates when using a conventional Kalman filter algorithm. Schmidt introduces the ``consider'' analysis to account for errors in both the dynamic and measurement models due to the unknown biases. Although the Schmidt-Kalman filter ``considers'' the biases, the uncertain initial values and incorrect covariance matrices of the unknown biases still are not considered. To solve this problem, a multiple adaptive fading Schmidt-Kalman filter (MAFSKF) is designed by using the proposed multiple adaptive fading Kalman filter to mitigate the negative effects of the unknown biases in dynamic or measurement model. The performance of the MAFSKF algorithm is verified by simulation.
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 93D09
zbMATH DE Number: 7016885
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/623930
OpenAlex ID: W2103868531
Wikidata QID: Q59066663
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description: scientific article; zbMATH DE number 7016885