Modal parameter estimation using interacting Kalman filter

The focus of this paper is Bayesian modal parameter recursive estimation based on an interacting Kalman filter algorithm with decoupled distributions for frequency and damping. Interacting Kalman filter is a combination of two widely used Bayesian estimation methods: the particle filter and the Kalman filter. Some sensitivity analysis techniques are also proposed in order to deduce a recursive estimate of modal parameters from the estimates of the damping/stiffness coefficients.

► Kalman filters tracks state changes in linear systems.
► Particle filters extend Kalman filters in nonlinear models.
► Vibration models become nonlinear including the state matrices into the state.
► Coupling Kalman and particle filtering reduce dimension problems.
► Decoupling frequency and damping probability law improve estimation accuracy.