IEICE TRANSACTIONS on Fundamentals
Design of Recursive Wiener Smoother Given Covariance Information
Summary :
This paper discusses the fixed-point smoothing and filtering problems given lumped covariance function of a scalar signal process observed with additive white Gaussian noise. The recursive Wiener smoother and filter are derived by applying an invariant imbedding method to the Volterra-type integral equation of the second kind in linear least-squares estimation problems. The resultant estimators in Theorem 2 require the information of the crossvariance function of the state variable with the observed value, the system matrix, the observation vector, the variance of the observation noise and the observed value. Here, it is assumed that the signal process is generated by the state-space model. The spectral factorization problem is also considered in Sects. 1 and 2.
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- IEICE TRANSACTIONS on Fundamentals Vol.E79-A No.6 pp.864-872
- Publication Date
- 1996/06/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
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Seiichi NAKAMORI, "Design of Recursive Wiener Smoother Given Covariance Information" in IEICE TRANSACTIONS on Fundamentals,
vol. E79-A, no. 6, pp. 864-872, June 1996, doi: .
Abstract: This paper discusses the fixed-point smoothing and filtering problems given lumped covariance function of a scalar signal process observed with additive white Gaussian noise. The recursive Wiener smoother and filter are derived by applying an invariant imbedding method to the Volterra-type integral equation of the second kind in linear least-squares estimation problems. The resultant estimators in Theorem 2 require the information of the crossvariance function of the state variable with the observed value, the system matrix, the observation vector, the variance of the observation noise and the observed value. Here, it is assumed that the signal process is generated by the state-space model. The spectral factorization problem is also considered in Sects. 1 and 2.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e79-a_6_864/_p
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@ARTICLE{e79-a_6_864,
author={Seiichi NAKAMORI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Design of Recursive Wiener Smoother Given Covariance Information},
year={1996},
volume={E79-A},
number={6},
pages={864-872},
abstract={This paper discusses the fixed-point smoothing and filtering problems given lumped covariance function of a scalar signal process observed with additive white Gaussian noise. The recursive Wiener smoother and filter are derived by applying an invariant imbedding method to the Volterra-type integral equation of the second kind in linear least-squares estimation problems. The resultant estimators in Theorem 2 require the information of the crossvariance function of the state variable with the observed value, the system matrix, the observation vector, the variance of the observation noise and the observed value. Here, it is assumed that the signal process is generated by the state-space model. The spectral factorization problem is also considered in Sects. 1 and 2.},
keywords={},
doi={},
ISSN={},
month={June},}
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TY - JOUR
TI - Design of Recursive Wiener Smoother Given Covariance Information
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 864
EP - 872
AU - Seiichi NAKAMORI
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E79-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 1996
AB - This paper discusses the fixed-point smoothing and filtering problems given lumped covariance function of a scalar signal process observed with additive white Gaussian noise. The recursive Wiener smoother and filter are derived by applying an invariant imbedding method to the Volterra-type integral equation of the second kind in linear least-squares estimation problems. The resultant estimators in Theorem 2 require the information of the crossvariance function of the state variable with the observed value, the system matrix, the observation vector, the variance of the observation noise and the observed value. Here, it is assumed that the signal process is generated by the state-space model. The spectral factorization problem is also considered in Sects. 1 and 2.
ER -
English
