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Proceedings Paper

Wavelet-based compression of covariances in Kalman filtering of geophysical flows
Author(s): Toshio Mike Chin; Arthur J. Mariano
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Paper Abstract

The covariance matrix in Kalman filter is reduced using compactly supported orthonormal wavelet transform and is parameterized by only O(N) coefficients, where N is the dimension of the state vector. An approximate filtering algorithm, in which the covariances remain in such a transformed and compressed form throughout the time recursion, is designed. For estimation of space-time processes characteristic of geophysical flows, the proposed algorithm performs near optimally, while reducing computational and storage requirements of Kalman filter.

Paper Details

Date Published: 15 March 1994
PDF: 9 pages
Proc. SPIE 2242, Wavelet Applications, (15 March 1994); doi: 10.1117/12.170083
Show Author Affiliations
Toshio Mike Chin, Univ. of Miami (United States)
Arthur J. Mariano, Univ. of Miami (United States)


Published in SPIE Proceedings Vol. 2242:
Wavelet Applications
Harold H. Szu, Editor(s)

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