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) |
|---|---|
| Title of host publication | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Pages | 2785-2788 |
| Number of pages | 4 |
| Volume | 5 |
| State | Published - 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 |
Other
| Other | Proceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5) |
|---|---|
| City | Detroit, MI, USA |
| Period | 5/9/95 → 5/12/95 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Signal Processing
- Acoustics and Ultrasonics
Cite this
Kalman filtering of large-scale geophysical flows by approximations based on Markov random field and wavelet. / Chin, Toshio M.; Mariano, Arthur J.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 5 1995. p. 2785-2788.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Kalman filtering of large-scale geophysical flows by approximations based on Markov random field and wavelet
AU - Chin, Toshio M.
AU - Mariano, Arthur J
PY - 1995
Y1 - 1995
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0028996466&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0028996466&partnerID=8YFLogxK
M3 - Conference contribution
VL - 5
SP - 2785
EP - 2788
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -