Abstract
Large-scale extended Kalman filters for atmospheric and oceanic circulation models can readily be approximated using a wavelet transform or a Markov random field model. For a filtering problem where the unknown field of the state variables is highly correlated and the observations are relatively sparse, the wavelet-approximated filter seems more appropriate. For a problem in which the covariance matrix is non-singular and a relatively large quantity of independent observations are processed, the MRF-approximated filter seems more appropriate.
Original language | English (US) |
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Pages (from-to) | 2785-2788 |
Number of pages | 4 |
Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
Volume | 5 |
State | Published - Jan 1 1995 |
Event | Proceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5) - Detroit, MI, USA Duration: May 9 1995 → May 12 1995 |
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering