Fixed-Interval Smoothing from Uncertain Observations with White Plus Coloured Noises Using Covariance Information
Summary :
This paper presents recursive algorithms for the least mean-squared error linear filtering and fixed-interval smoothing estimators, from uncertain observations for the case of white and white plus coloured observation noises. The estimators are obtained by an innovation approach and do not use the state-space model, but only covariance information about the signal and the observation noises, as well as the probability that the signal exists in the observed values. Therefore the algorithms are applicable not only to signal processes that can be estimated by the conventional formulation using the state-space model but also to those for which a realization of the state-space model is not available. It is assumed that both the signal and the coloured noise autocovariance functions are expressed in a semi-degenerate kernel form. Since the semi-degenerate kernel is suitable for expressing autocovariance functions of non-stationary or stationary signal processes, the proposed estimators provide estimates of general signal processes.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E87-A No.5 pp.1209-1218
- Publication Date
- 2004/05/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
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Seiichi NAKAMORI, Raquel CABALLERO-AGUILA, Aurora HERMOSO-CARAZO, Josefa LINARES-PEREZ, "Fixed-Interval Smoothing from Uncertain Observations with White Plus Coloured Noises Using Covariance Information" in IEICE TRANSACTIONS on Fundamentals,
vol. E87-A, no. 5, pp. 1209-1218, May 2004, doi: .
Abstract: This paper presents recursive algorithms for the least mean-squared error linear filtering and fixed-interval smoothing estimators, from uncertain observations for the case of white and white plus coloured observation noises. The estimators are obtained by an innovation approach and do not use the state-space model, but only covariance information about the signal and the observation noises, as well as the probability that the signal exists in the observed values. Therefore the algorithms are applicable not only to signal processes that can be estimated by the conventional formulation using the state-space model but also to those for which a realization of the state-space model is not available. It is assumed that both the signal and the coloured noise autocovariance functions are expressed in a semi-degenerate kernel form. Since the semi-degenerate kernel is suitable for expressing autocovariance functions of non-stationary or stationary signal processes, the proposed estimators provide estimates of general signal processes.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e87-a_5_1209/_p
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@ARTICLE{e87-a_5_1209,
author={Seiichi NAKAMORI, Raquel CABALLERO-AGUILA, Aurora HERMOSO-CARAZO, Josefa LINARES-PEREZ, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Fixed-Interval Smoothing from Uncertain Observations with White Plus Coloured Noises Using Covariance Information},
year={2004},
volume={E87-A},
number={5},
pages={1209-1218},
abstract={This paper presents recursive algorithms for the least mean-squared error linear filtering and fixed-interval smoothing estimators, from uncertain observations for the case of white and white plus coloured observation noises. The estimators are obtained by an innovation approach and do not use the state-space model, but only covariance information about the signal and the observation noises, as well as the probability that the signal exists in the observed values. Therefore the algorithms are applicable not only to signal processes that can be estimated by the conventional formulation using the state-space model but also to those for which a realization of the state-space model is not available. It is assumed that both the signal and the coloured noise autocovariance functions are expressed in a semi-degenerate kernel form. Since the semi-degenerate kernel is suitable for expressing autocovariance functions of non-stationary or stationary signal processes, the proposed estimators provide estimates of general signal processes.},
keywords={},
doi={},
ISSN={},
month={May},}
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TY - JOUR
TI - Fixed-Interval Smoothing from Uncertain Observations with White Plus Coloured Noises Using Covariance Information
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1209
EP - 1218
AU - Seiichi NAKAMORI
AU - Raquel CABALLERO-AGUILA
AU - Aurora HERMOSO-CARAZO
AU - Josefa LINARES-PEREZ
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E87-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2004
AB - This paper presents recursive algorithms for the least mean-squared error linear filtering and fixed-interval smoothing estimators, from uncertain observations for the case of white and white plus coloured observation noises. The estimators are obtained by an innovation approach and do not use the state-space model, but only covariance information about the signal and the observation noises, as well as the probability that the signal exists in the observed values. Therefore the algorithms are applicable not only to signal processes that can be estimated by the conventional formulation using the state-space model but also to those for which a realization of the state-space model is not available. It is assumed that both the signal and the coloured noise autocovariance functions are expressed in a semi-degenerate kernel form. Since the semi-degenerate kernel is suitable for expressing autocovariance functions of non-stationary or stationary signal processes, the proposed estimators provide estimates of general signal processes.
ER -
English
