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