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15 March 1994 Wavelet-based compression of covariances in Kalman filtering of geophysical flows
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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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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