Bayesian state and parameter estimation of uncertain dynamical systems
Resource
Probabilistic Engineering Mechanics,21(1),81-96.
Journal
Probabilistic Engineering Mechanics
Journal Volume
21
Journal Issue
1
Pages
81-96
Date Issued
2006
Date
2006
Author(s)
Abstract
The focus of this paper is Bayesian state and parameter estimation using nonlinear models. A recently developed method, the particle filter, is studied that is based on stochastic simulation. Unlike the well-known extended Kalman filter, the particle filter is applicable to highly nonlinear models with non-Gaussian uncertainties. Recently developed techniques that improve the convergence of the particle filter simulations are introduced and discussed. Comparisons between the particle filter and the extended Kalman filter are made using several numerical examples of nonlinear systems. The results indicate that the particle filter provides consistent state and parameter estimates for highly nonlinear models, while the extended Kalman filter does not. © 2005 Elsevier Ltd. All rights reserved.
Type
journal article