Fearghal O'Donncha, Scott C. James
OCEANS 2017 Aberdeen
This paper presents a new iterative state estimation algorithm for advection dominated flows with non-Gaussian uncertainty description of L∞-type: uncertain initial condition and model error are assumed to be pointwise bounded in space and time, and the observation noise has uncertain but bounded second moments. The algorithm approximates this L∞-type bounding set by a union of possibly overlapping ellipsoids, which are localised (in space) on a number of sub-domains. On each sub-domain the state of the original system is estimated by the standard L2-type filter (e.g. Kalman minimax filter) which uses Gaussian/ellipsoidal uncertainty description and observations (if any) which correspond to this sub-domain. The resulting local state estimates are stitched together by the iterative d-ADN Schwarz method to reconstruct the state of the original system. The efficacy of the proposed method is demonstrated with a set of numerical examples.
Fearghal O'Donncha, Scott C. James
OCEANS 2017 Aberdeen
Fearghal O'Donncha, Michael Hartnett, et al.
Journal of Marine Systems
Fearghal O'Donncha, Scott C. James, et al.
OCEANS 2015
Emanuele Ragnoli, Sergiy Zhuk, et al.
CDC 2015