Kalman filtering of large-scale geophysical flows by approximations based on Markov random field and wavelet

Toshio M. Chin; Arthur J. Mariano

Kalman filtering of large-scale geophysical flows by approximations based on Markov random field and wavelet

Toshio M. Chin, Arthur J. Mariano

Ocean Sciences

Research output: Contribution to journal › Conference article

2 Scopus citations

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)
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

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Chin, T. M., & Mariano, A. J. (1995). Kalman filtering of large-scale geophysical flows by approximations based on Markov random field and wavelet. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 5, 2785-2788.

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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.",

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AU - Chin, Toshio M.

AU - Mariano, Arthur J.

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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.

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M3 - Conference article

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JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

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T2 - Proceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5)

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