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2008
IEEE

Ensemble-on-demand Kalman filter for large-scale systems with time-sparse measurements

9 years 8 months ago
Ensemble-on-demand Kalman filter for large-scale systems with time-sparse measurements
The ensemble Kalman filter for data assimilation involves the propagation of a collection of ensemble members. Under the assumption of time-sparse measurements, we avoid propagating the ensemble members for all of the time steps by creating an ensemble of models only when a new measurement is made available. We call this algorithm the ensembleon-demand Kalman filter (EnODKF). We use guidelines for ensemble size within the context of EnODKF, and demonstrate the performance of EnODKF for a representative example, specifically, a heat flow problem.
In Sung Kim, Bruno Otávio Soares Teixeira,
Added 07 Dec 2010
Updated 07 Dec 2010
Type Conference
Year 2008
Where CDC
Authors In Sung Kim, Bruno Otávio Soares Teixeira, Dennis S. Bernstein
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