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Online estimation of noise parameters for Kalman filter
Yuen K.-V.; Liang P.-F.; Kuok S.-C.
2013
Source PublicationStructural Engineering and Mechanics
ISSN12254568
Volume47Issue:3Pages:361
Abstract

A Bayesian probabilistic method is proposed for online estimation of the process noise and measurement noise parameters for Kalman filter. Kalman filter is a well-known recursive algorithm for state estimation of dynamical systems. In this algorithm, it is required to prescribe the covariance matrices of the process noise and measurement noise. However, inappropriate choice of these covariance matrices substantially deteriorates the performance of the Kalman filter. In this paper, a probabilistic method is proposed for online estimation of the noise parameters which govern the noise covariance matrices. The proposed Bayesian method not only estimates the optimal noise parameters but also quantifies the associated estimation uncertainty in an online manner. By utilizing the estimated noise parameters, reliable state estimation can be accomplished. Moreover, the proposed method does not assume any stationarity condition of the process noise and/or measurement noise. By removing the stationarity constraint, the proposed method enhances the applicability of the state estimation algorithm for nonstationary circumstances generally encountered in practice. To illustrate the efficacy and efficiency of the proposed method, examples using a fifty-story building with different stationarity scenarios of the process noise and measurement noise are presented. Copyright © 2013 Techno-Press, Ltd.

KeywordBayesian Probabilistic Approach Kalman Filter Measurement Noise Online Algorithm Process Noise Structural Health Monitoring
DOI10.12989/sem.2013.47.3.361
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEngineering
WOS SubjectEngineering, Civil ; Engineering, Mechanical
WOS IDWOS:000325332800004
The Source to ArticleScopus
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被引频次[WOS]:26   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
AffiliationUniv Macau, Fac Sci & Technol, Dept Civil & Environm Engn, Taipa, Peoples R China
First Author AffilicationUniversity of Macau
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Yuen K.-V.,Liang P.-F.,Kuok S.-C.. Online estimation of noise parameters for Kalman filter[J]. Structural Engineering and Mechanics,2013,47(3):361.
APA Yuen K.-V.,Liang P.-F.,&Kuok S.-C..(2013).Online estimation of noise parameters for Kalman filter.Structural Engineering and Mechanics,47(3),361.
MLA Yuen K.-V.,et al."Online estimation of noise parameters for Kalman filter".Structural Engineering and Mechanics 47.3(2013):361.
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