Structural response reconstruction method based on residual adaptive Kalman filtering
XUE Yifan
YIN Hong
QI Yibo
PENG Zhenrui
Abstract：[Objective]Aiming at the problem that the traditional Kalman filtering algorithm applied in the process of structural response reconstruction needs to preset the measurement noise covariance to a constant value,which leads to low reconstruction accuracy or even non-convergence of filtering,a structural response reconstruction method based on residual adaptive Kalman filtering algorithm was proposed.[Methods]Firstly,the residual theory was combined with the covariance matching technique and the moving window method was used to approximate the residual terms in the covariance matching formulae to achieve adaptive estimation of the Kalman gain and measurement noise covariance.Then,the acceleration data were used to reconstruct the different types of responses at each position of the structure.Finally,the proposed method was validated through a numerical example of a two-dimensional truss and an experimental example of a simply supported beam,comparing the response reconstruction effects with those of Kalman filtering and moving window Kalman filtering algorithms.[Results]The results show that the proposed algorithm can adaptively estimate the measurement noise covariance close to the true value and achieves higher accuracy in structural response reconstruction compared to non-adaptive methods.
Keywords：ResidualCovariance matching technologyMoving window methodAdaptive Kalman filteringStructural response reconstruction
Publication Date：2026-06-30
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：7( 45-51 )