### 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 |

### Fingerprint

### ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Signal Processing
- Acoustics and Ultrasonics

### Cite this

*ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings*(Vol. 5, pp. 2785-2788)

**Kalman filtering of large-scale geophysical flows by approximations based on Markov random field and wavelet.** / Chin, Toshio M.; Mariano, Arthur J.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings.*vol. 5, pp. 2785-2788, Proceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5), Detroit, MI, USA, 5/9/95.

}

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 -