Unscented Kalman filtering for nonlinear structural dynamics
نویسندگان
چکیده
Joint estimation of unknown model parameters and unobserved state componentsfor stochastic, nonlinear dynamic systems is customarily pursued via the extendedKalman filter (EKF). However, in the presence of severe nonlinearities in the equa-tions governing system evolution, the EKF can become unstable and accuracy ofthe estimates gets poor. To improve the results, in this paper we account for re-cent developments in the field of statistical linearization and propose an unscentedKalman filtering procedure. In the case of softening single degree-of-freedom struc-tural systems, we show that the performance of the unscented Kalman filter (UKF),in terms of state tracking and model calibration, is significantly superior to that ofthe EKF.
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