[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.12989/sem.2013.47.3.361

Online estimation of noise parameters for Kalman filter

Yuen, Ka-Veng (Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau)
Liang, Peng-Fei (Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau)
Kuok, Sin-Chi (Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau)

Publication Information

Structural Engineering and Mechanics / v.47, no.3, 2013 , pp. 361-381 More about this Journal

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.

Keywords

Bayesian probabilistic approach; Kalman filter; online algorithm; process noise; measurement noise; structural health monitoring;

Citations & Related Records

Times Cited By KSCI : 4 (Citation Analysis)

Reference
Cited By KSCI

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