Wavelet-based compression of covariances in Kalman filtering of geophysical flows
Toshio Michael Chin, Arthur J. Mariano · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
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.