Identifying Optimal Measurement Subspace for the Ensemble Kalman Filter
To reduce the computational load of the ensemble Kalman filter while maintaining its efficacy, an optimization algorithm based on the generalized eigenvalue decomposition method is proposed for identifying the most informative measurement subspace. When the number of measurements is large, the proposed algorithm can be used to make an effective tradeoff between computational complexity and estimation accuracy. This algorithm also can be extended to other Kalman filters for measurement subspace selection.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1045111
- Report Number(s):
- PNNL-SA-84078; ELLEAK; KJ0401000; TRN: US201214%%939
- Journal Information:
- Electronics Letters, Vol. 48, Issue 11; ISSN 0013-5194
- Country of Publication:
- United States
- Language:
- English
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