Wavelet-based compression of covariances in Kalman filtering of geophysical flows

Toshio Mike Chin; Arthur J. Mariano

doi:10.1117/12.170083

15 March 1994 Wavelet-based compression of covariances in Kalman filtering of geophysical flows

Toshio Mike Chin, Arthur J. Mariano

Proceedings Volume 2242, Wavelet Applications; (1994) https://doi.org/10.1117/12.170083
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

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.

Citation Download Citation

Toshio Mike Chin and Arthur J. Mariano "Wavelet-based compression of covariances in Kalman filtering of geophysical flows", Proc. SPIE 2242, Wavelet Applications, (15 March 1994); https://doi.org/10.1117/12.170083

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