Performance of unscented Kalman filter for model updating with experimental data

Abstract The unscented Kalman filter (UKF) is one of the most widely used algorithms for identifying and updating numerical model parameters. When updating from experimental data, the UKF performs well but it is sensitive to the selection of the initial algorithm variables and vulnerable to the influence of measurement noise. Furthermore, the ability to capture new behavioral features, such as hardening at large displacements, is a challenge. …