Particle and sigma-point Kalman filters: a comparison of performance

Aiming to track the full state of a nonlinearly evolving structural system and simultaneously calibrate its constitutive model, in this paper we compare the performances of the sigma-point Kalman filter (S-PKF), of a standard particle filter (PF), and of an extended Kalman-particle filter (EK-PF). To ensure that structural responses do not affect the filters’ performances, we focus on a single degree-of-freedom (DOF) system and show that the three filters are all capable to track possible failure mechanisms. As far as model calibration is concerned, the newly proposed EK-PF performs better than the other two filters, providing unbiased parameter estimates even when the system is diverging because of a failure event.