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An efficient algorithm for estimating noise covariances in distributed systemsAn efficient computational algorithm for estimating the noise covariance matrices of large linear discrete stochatic-dynamic systems is presented. Such systems arise typically by discretizing distributed-parameter systems, and their size renders computational efficiency a major consideration. The proposed adaptive filtering algorithm is based on the ideas of Belanger, and is algebraically equivalent to his algorithm. The earlier algorithm, however, has computational complexity proportional to p to the 6th, where p is the number of observations of the system state, while the new algorithm has complexity proportional to only p-cubed. Further, the formulation of noise covariance estimation as a secondary filter, analogous to state estimation as a primary filter, suggests several generalizations of the earlier algorithm. The performance of the proposed algorithm is demonstrated for a distributed system arising in numerical weather prediction.
Document ID
19860029179
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Dee, D. P.
(New York University NY; Rio de Janeiro, Pontificia Universidade Catolica, Brazil)
Cohn, S. E.
(New York University NY, United States)
Ghil, M.
(Sigma Data Services Corp. Greenbelt, MD, United States)
Dalcher, A.
(New York University NY; California, University, Los Angeles, United States)
Date Acquired
August 12, 2013
Publication Date
November 1, 1985
Publication Information
Publication: IEEE Transactions on Automatic Control
Volume: AC-30
ISSN: 0018-9286
Subject Category
Numerical Analysis
Accession Number
86A13917
Funding Number(s)
CONTRACT_GRANT: NSG-5130
CONTRACT_GRANT: NAG5-341
CONTRACT_GRANT: NSG-5034
CONTRACT_GRANT: FINEP-4-3-82-0719-00
CONTRACT_GRANT: NSF INT-83-14934
CONTRACT_GRANT: CNPQ-10,11,0021/83
CONTRACT_GRANT: NSF MCS-82-01599
CONTRACT_GRANT: NOAA-NA-84AAD00018
Distribution Limits
Public
Copyright
Other

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