Application of the unscented Kalman filter for real-time nonlinear structural system identification

Over the past few decades, structural system identification based on vibration measurements has attracted much attention in the structural dynamics field. The well-known extended Kalman filter (EKF) is often used to deal with nonlinear system identification in many civil engineering applications. In spite of that, applying an EKF to highly nonlinear structural systems is not a trivial task, particularly those subject to severe loading. Unlike the EKF, a new technique, the unscented Kalman filter (UKF) is applicable to highly nonlinear systems. In this paper, the EKF and UKF are compared and applied for nonlinear structural system identification. Simulation results show that the UKF produces better state estimation and parameter identification than the EKF and is also more robust to measurement noise levels.