Nonlinear Estimation with Polynomial Chaos and Higher Order Moment Updates Conference Paper

abstract

• In this paper we present a nonlinear estimation algorithm that combines generalized polynomial chaos theory and higher moment updates. Polynomial chaos theory is used to predict the evolution of uncertainty of the nonlinear random process, and higher order moment updates are used to estimate the posterior non Gaussian probability density function of the random process. The moments are updated using a linear gain. The nonlinear estimation algorithm is then applied to the duffing oscillator system with initial condition uncertainty and its performance is compared with linear estimators based on extended Kalman filtering framework. We observe that this estimator outperforms the linear estimator when measurements are not available very frequently, thus highlighting the need for nonlinear estimator in such scenarios. 2010 AACC.

name of conference

• Proceedings of the 2010 American Control Conference

authors

• Bhattacharya, Raktim

published proceedings

• 2010 AMERICAN CONTROL CONFERENCE

author list (cited authors)

• Dutta, P., & Bhattacharya, R.

citation count

• 19

complete list of authors

• Dutta, Parikshit||Bhattacharya, Raktim

publication date

• January 2010

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings of the ... American Control Conference. American Control Conference  Journal

Digital Object Identifier (DOI)

• 10.1109/acc.2010.5531023

International Standard Book Number (ISBN) 13

• 9781424474264

start page

• 3142

end page

• 3147

URL

• http://dx.doi.org/10.1109/acc.2010.5531023