IEICE TRANSACTIONS on Fundamentals
Optimal Filtering Algorithm Using Covariance Information in Linear Continuous Distributed Parameter Systems
Summary :
This paper presents an optimal filtering algorithm using the covariance information in linear continuous distributed parameter systems. It is assumed that the signal is observed with additive white Gaussian noise. The autocovariance function of the signal, the variance of white Gaussian noise, the observed value and the observation matrix are used in the filtering algorithm. Then, the current filter has an advantage that it can be applied to the case where a partial differential equation, which generates the signal process, is unknown.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E77-A No.6 pp.1050-1057
- Publication Date
- 1994/06/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Control and Computing
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Seiichi NAKAMORI, "Optimal Filtering Algorithm Using Covariance Information in Linear Continuous Distributed Parameter Systems" in IEICE TRANSACTIONS on Fundamentals,
vol. E77-A, no. 6, pp. 1050-1057, June 1994, doi: .
Abstract: This paper presents an optimal filtering algorithm using the covariance information in linear continuous distributed parameter systems. It is assumed that the signal is observed with additive white Gaussian noise. The autocovariance function of the signal, the variance of white Gaussian noise, the observed value and the observation matrix are used in the filtering algorithm. Then, the current filter has an advantage that it can be applied to the case where a partial differential equation, which generates the signal process, is unknown.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e77-a_6_1050/_p
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@ARTICLE{e77-a_6_1050,
author={Seiichi NAKAMORI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Optimal Filtering Algorithm Using Covariance Information in Linear Continuous Distributed Parameter Systems},
year={1994},
volume={E77-A},
number={6},
pages={1050-1057},
abstract={This paper presents an optimal filtering algorithm using the covariance information in linear continuous distributed parameter systems. It is assumed that the signal is observed with additive white Gaussian noise. The autocovariance function of the signal, the variance of white Gaussian noise, the observed value and the observation matrix are used in the filtering algorithm. Then, the current filter has an advantage that it can be applied to the case where a partial differential equation, which generates the signal process, is unknown.},
keywords={},
doi={},
ISSN={},
month={June},}
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TY - JOUR
TI - Optimal Filtering Algorithm Using Covariance Information in Linear Continuous Distributed Parameter Systems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1050
EP - 1057
AU - Seiichi NAKAMORI
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E77-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 1994
AB - This paper presents an optimal filtering algorithm using the covariance information in linear continuous distributed parameter systems. It is assumed that the signal is observed with additive white Gaussian noise. The autocovariance function of the signal, the variance of white Gaussian noise, the observed value and the observation matrix are used in the filtering algorithm. Then, the current filter has an advantage that it can be applied to the case where a partial differential equation, which generates the signal process, is unknown.
ER -
English
