Stochastic system identification via particle and sigma-point

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 particlefilter (pf). since a formal proof of the effects of softening in a stochastic structural system on the accuracyand stability of the filters is still missing, we comparatively assess the performances of s-pkf and pf. weshow that the pf displays a higher convergence rate towards steady-state model calibrations and the s-pkfis less sensitive to the measurement noise. both s-pkf and pf are robust, even if they tend to get unstablewhen a structural failure is triggered.