HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Reduced-order Unscented Kalman Filtering with application to parameter identification in large-dimensional systems

Abstract : We propose a general reduced-order filtering strategy adapted to Unscented Kalman Filtering for any choice of sampling points distribution. This provides tractable filtering algorithms which can be used with large-dimensional systems when the uncertainty space is of reduced size, and these algorithms only invoke the original dynamical and observation operators, namely, they do not require tangent operator computations, which of course is of considerable benefit when nonlinear operators are considered. The algorithms are derived in discrete time as in the classical UKF formalism - well-adapted to time discretized dynamical equations - and then extended into consistent continuous-time versions. This reduced-order filtering approach can be used in particular for the estimation of parameters in large dynamical systems arising from the discretization of partial differential equations, when state estimation can be handled by an adequate Luenberger observer inspired from feedback control. In this case, we give an analysis of the joint state-parameter estimation procedure based on linearized error, and we illustrate the effectiveness of the approach using a test problem inspired from cardiac biomechanics.
Document type :
Journal articles
Complete list of metadata

Cited literature [23 references]  Display  Hide  Download

Contributor : Dominique Chapelle Connect in order to contact the contributor
Submitted on : Thursday, December 30, 2010 - 10:03:00 AM
Last modification on : Thursday, February 3, 2022 - 11:13:52 AM
Long-term archiving on: : Thursday, March 31, 2011 - 2:38:35 AM


Publisher files allowed on an open archive




Philippe Moireau, Dominique Chapelle. Reduced-order Unscented Kalman Filtering with application to parameter identification in large-dimensional systems. ESAIM: Control, Optimisation and Calculus of Variations, EDP Sciences, 2011, 17 (2), pp.380-405. ⟨10.1051/cocv/2010006⟩. ⟨inria-00550104⟩



Record views


Files downloads