Identifying optimal measurement subspace for ensemble Kalman filter

N. Zhou; Z. Huang; G. Welch; J. Zhang

doi:10.1049/el.2012.0833

Identifying optimal measurement subspace for ensemble Kalman filter

N. Zhou
, Z. Huang
, G. Welch
, J. Zhang

Electrical and Computer Eng

Binghamton University

Pacific Northwest National Laboratory
University of North Carolina at Chapel Hill
University of Central Florida

Research output: Contribution to journal › Article › peer-review

Abstract

To reduce the computational load of the ensemble Kalman filter while maintaining its efficacy, an optimisation algorithm based on the generalised 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 trade-off between computational complexity and estimation accuracy.

Original language	English
Pages (from-to)	618-620
Number of pages	3
Journal	Electronics Letters
Volume	48
Issue number	11
DOIs	https://doi.org/10.1049/el.2012.0833
State	Published - May 24 2012

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10.1049/el.2012.0833

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title = "Identifying optimal measurement subspace for ensemble Kalman filter",

abstract = "To reduce the computational load of the ensemble Kalman filter while maintaining its efficacy, an optimisation algorithm based on the generalised 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 trade-off between computational complexity and estimation accuracy.",

author = "N. Zhou and Z. Huang and G. Welch and J. Zhang",

year = "2012",

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AU - Zhou, N.

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AU - Welch, G.

AU - Zhang, J.

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N2 - To reduce the computational load of the ensemble Kalman filter while maintaining its efficacy, an optimisation algorithm based on the generalised 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 trade-off between computational complexity and estimation accuracy.

AB - To reduce the computational load of the ensemble Kalman filter while maintaining its efficacy, an optimisation algorithm based on the generalised 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 trade-off between computational complexity and estimation accuracy.

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U2 - 10.1049/el.2012.0833

DO - 10.1049/el.2012.0833

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SN - 0013-5194

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JO - Electronics Letters

JF - Electronics Letters

IS - 11

ER -

Identifying optimal measurement subspace for ensemble Kalman filter

Abstract

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