Performance of nonlinear filters for noise-driven chaotic oscillatory systems
Khalil, M., Sarkar, A. and Adhikari, S.
49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics
& Materials Conference,Schaumburg, IL, USA, April 2008.
Combined state and parameter estimation of dynamical systems plays an
important role in many branches of applied science and engineering. A wide
variety of methods have been developed to tackle the joint state and parameter
estimation problem. The Extended Kalman Filter (EKF) method is a popular
approach which combines the traditional Kalman filtering and linearisation
techniques to effectively tackle weakly nonlinear and non-Gaussian problems. Its
mathematical formulation is based on the assumption that the probability density
function (PDF) of the state vector can be reasonably approximated to be
Gaussian. Recent investigations have been focused on Monte Carlo based sampling
algorithms in dealing with strongly nonlinear and non-Gaussian models. Of
particular interest is the Ensemble Kalman Filter (EnKF) and the Particle Filter
(PF). These methods are robust in handling general forms of nonlinearities and
non-Gaussian models, albeit with higher computational costs. In this paper we
report the joint state and parameter estimation of noise-driven oscillatory
systems undergoing limit cycle oscillation using EKF, EnKF and PF.
BiBTeX Entry
@INPROCEEDINGS{cp46,
AUTHOR={M. Khalil and A. Sarkar and S. Adhikari},
TITLE={Performance of nonlinear filters for noise-driven chaotic oscillatory systems},
BOOKTITLE={49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics \& Materials Conference},
YEAR={2008},
Address={Schaumburg, IL, USA},
Month={April},
Organization={AIAA},
Note={}
}
by Sondipon Adhikari