Derivation of fixed-interval smoothing algorithm using covariance information in distributed parameter systems
This paper proposes a recursive least mean squared error fixed-interval smoothing algorithm in distributed parameter systems. It is assumed that the state-space model of the signal to be estimated is unknown, and the algorithm only requires the second-order moments of the signal and the white noise...
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Published in | Applied mathematics and computation Vol. 176; no. 2; pp. 662 - 672 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
New York, NY
Elsevier Inc
15.05.2006
Elsevier |
Subjects | |
Online Access | Get full text |
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Summary: | This paper proposes a recursive least mean squared error fixed-interval smoothing algorithm in distributed parameter systems. It is assumed that the state-space model of the signal to be estimated is unknown, and the algorithm only requires the second-order moments of the signal and the white noise perturbing its observations. Practical application of the proposed algorithm is illustrated with a restoration image problem. |
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ISSN: | 0096-3003 1873-5649 |
DOI: | 10.1016/j.amc.2005.10.012 |