Stochastic system identification via particle and sigma-point Kalman filtering

In this paper, joint identification for structural systems, characterized by severe nonlinearities (softening) in the constitutive model, is pursued via the Sigma-Point Kalman Filter (S-PKF) and the Particle Filter (PF). Since a formal proof of the effects of softening in a stochastic structural system on the accuracy and stability of the filters is still missing, we comparatively assess the performances of S-PKF and PF. We show that the PF displays a higher convergence rate towards steady-state model calibrations and the S-PKF is less sensitive to the measurement noise. Both S-PKF and PF are robust, even if they tend to get unstable when a structural failure is triggered.