Identifying optimal measurement subspace for ensemble Kalman filter

    Authors

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

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Electron. Lett.

    Keywords

    Engineering, Electrical & Electronic

    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.

    Journal Title

    Electronics Letters

    Volume

    48

    Issue/Number

    11

    Publication Date

    1-1-2012

    Document Type

    Article

    Language

    English

    First Page

    618

    Last Page

    620

    WOS Identifier

    WOS:000304421900012

    ISSN

    0013-5194

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