The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article propose une nouvelle méthode de conception d'algorithmes de filtrage non linéaire et de lissage à virgule fixe dans les systèmes stochastiques à temps discret. La valeur observée est constituée d'un signal modulé de manière non linéaire et d'un bruit d'observation gaussien blanc additif. Les algorithmes de filtrage et de lissage en virgule fixe sont conçus sur la même idée que le filtre de Kalman étendu dérivé du filtre de Kalman des moindres carrés récursif dans les systèmes stochastiques linéaires à temps discret. Le filtre et le lisseur à virgule fixe proposés nécessitent l'information de la fonction d'autocovariance du signal, de la variance du bruit d'observation, de la fonction d'observation non linéaire et de sa fonction différenciée par rapport au signal. La précision de l'estimation du filtre étendu proposé est comparée théoriquement au filtre étendu maximum a posteriori (MAP). En outre, la précision de l'estimation des estimateurs actuels est comparée numériquement aux estimateurs MAP étendus, aux estimateurs de Kalman étendus et à la méthode de neuro-informatique de Kalman.
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Seiichi NAKAMORI, "Design of Estimators Using Covariance Information in Discrete-Time Stochastic Systems with Nonlinear Observation Mechanism" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 7, pp. 1292-1304, July 1999, doi: .
Abstract: This paper proposes a new design method of nonlinear filtering and fixed-point smoothing algorithms in discrete-time stochastic systems. The observed value consists of nonlinearly modulated signal and additive white Gaussian observation noise. The filtering and fixed-point smoothing algorithms are designed based on the same idea as the extended Kalman filter derived based on the recursive least-squares Kalman filter in linear discrete-time stochastic systems. The proposed filter and fixed-point smoother necessitate the information of the autocovariance function of the signal, the variance of the observation noise, the nonlinear observation function and its differentiated one with respect to the signal. The estimation accuracy of the proposed extended filter is compared with the extended maximum a posteriori (MAP) filter theoretically. Also, the current estimators are compared in estimation accuracy with the extended MAP estimators, the extended Kalman estimators and the Kalman neuro computing method numerically.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_7_1292/_p
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@ARTICLE{e82-a_7_1292,
author={Seiichi NAKAMORI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Design of Estimators Using Covariance Information in Discrete-Time Stochastic Systems with Nonlinear Observation Mechanism},
year={1999},
volume={E82-A},
number={7},
pages={1292-1304},
abstract={This paper proposes a new design method of nonlinear filtering and fixed-point smoothing algorithms in discrete-time stochastic systems. The observed value consists of nonlinearly modulated signal and additive white Gaussian observation noise. The filtering and fixed-point smoothing algorithms are designed based on the same idea as the extended Kalman filter derived based on the recursive least-squares Kalman filter in linear discrete-time stochastic systems. The proposed filter and fixed-point smoother necessitate the information of the autocovariance function of the signal, the variance of the observation noise, the nonlinear observation function and its differentiated one with respect to the signal. The estimation accuracy of the proposed extended filter is compared with the extended maximum a posteriori (MAP) filter theoretically. Also, the current estimators are compared in estimation accuracy with the extended MAP estimators, the extended Kalman estimators and the Kalman neuro computing method numerically.},
keywords={},
doi={},
ISSN={},
month={July},}
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TY - JOUR
TI - Design of Estimators Using Covariance Information in Discrete-Time Stochastic Systems with Nonlinear Observation Mechanism
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1292
EP - 1304
AU - Seiichi NAKAMORI
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 1999
AB - This paper proposes a new design method of nonlinear filtering and fixed-point smoothing algorithms in discrete-time stochastic systems. The observed value consists of nonlinearly modulated signal and additive white Gaussian observation noise. The filtering and fixed-point smoothing algorithms are designed based on the same idea as the extended Kalman filter derived based on the recursive least-squares Kalman filter in linear discrete-time stochastic systems. The proposed filter and fixed-point smoother necessitate the information of the autocovariance function of the signal, the variance of the observation noise, the nonlinear observation function and its differentiated one with respect to the signal. The estimation accuracy of the proposed extended filter is compared with the extended maximum a posteriori (MAP) filter theoretically. Also, the current estimators are compared in estimation accuracy with the extended MAP estimators, the extended Kalman estimators and the Kalman neuro computing method numerically.
ER -