Affiliated with RCfalse
Status即將出版Forthcoming
Estimation of time-varying noise parameters for unscented Kalman filter
Yuen, Ka Veng1,2; Liu, Yu Song1,2; Yan, Wang Ji1,2
2022-11-15
Source PublicationMechanical Systems and Signal Processing
ISSN0888-3270
Volume180
Abstract

The unscented Kalman filter (UKF) is a promising method for system state and structural parameters estimation. However, its performance depends on the process noise and measurement noise covariance matrices, which are usually unknown in practice. Arbitrary selection of these covariance matrices may lead to unreliable or even diverging estimation results. To resolve this critical problem, we propose a Bayesian probabilistic algorithm for the estimation of the noise covariance matrices based on the response measurement. The proposed Noise-Parameters-Identified Unscented Kalman Filter (NPI-UKF) has the following salient features: (1) the divergence problem is resolved; (2) reliable estimation results including uncertainty quantification can be obtained; and (3) NPI-UKF is applicable to nonstationary situations. These salient features are illustrated through the numerical applications to a bridge structure and a laboratory experiment to a shear building model. The efficacy and robustness of NPI-UKF will be validated.

KeywordBayesian Inference Noise Covariance Matrices Structural Health Monitoring System Identification Unscented Kalman Filter
DOI10.1016/j.ymssp.2022.109439
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Mechanical
WOS IDWOS:000833408600003
Scopus ID2-s2.0-85132763547
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Faculty of Science and Technology
Corresponding AuthorYuen, Ka Veng; Yan, Wang Ji
Affiliation1.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau, China
2.Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yuen, Ka Veng,Liu, Yu Song,Yan, Wang Ji. Estimation of time-varying noise parameters for unscented Kalman filter[J]. Mechanical Systems and Signal Processing,2022,180.
APA Yuen, Ka Veng,Liu, Yu Song,&Yan, Wang Ji.(2022).Estimation of time-varying noise parameters for unscented Kalman filter.Mechanical Systems and Signal Processing,180.
MLA Yuen, Ka Veng,et al."Estimation of time-varying noise parameters for unscented Kalman filter".Mechanical Systems and Signal Processing 180(2022).
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