Stochastic system identification using Kalman filtering

Simultaneous state tracking and calibration of constitutive laws for stochastic structural systems is usually pursued via the extended Kalman filter. However, in the presence of severe nonlinearities due to damage inception and growth, filtering may become unstable. To improve outcomes, a statistical linearization of the system equations has been recently adopted within the sigma-point Kalman filtering approach. In this study we compare the performances of the extended and sigma-point Kalman filters, and show that the latter one is superior in calibrating softening materials laws.