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

Toshio M. Chin, Arthur J. Mariano

Research output: Contribution to journalConference 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 languageEnglish (US)
Pages (from-to)2785-2788
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
StatePublished - Jan 1 1995
EventProceedings of the 1995 20th International Conference on Acoustics, Speech, and Signal Processing. Part 2 (of 5) - Detroit, MI, USA
Duration: May 9 1995May 12 1995

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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