Bayesian state and parameter estimation of uncertain dynamical systems |
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| Authors: | Jianye Ching James L Beck Keith A Porter |
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| Affiliation: | aDepartment of Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC;bDepartment of Applied Mechanics and Civil Engineering, California Institute of Technology, Mail Code 104-44, Pasadena, CA 91125, USA;cGeorge W. Housner Senior Researcher in Civil Engineering, Mail Code 104-44, California Institute of Technology, Pasadena, CA 91125, USA |
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| 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. |
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| Keywords: | Bayesian analysis State estimation Parameter estimation Dynamical systems Monte Carlo simulation Importance sampling Particle filter Stochastic simulation Extended Kalman filter |
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