IEICE TRANSACTIONS on Fundamentals
Design of Estimators Using Covariance Information in Discrete-Time Stochastic Systems with Nonlinear Observation Mechanism
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E82-A No.7 pp.1292-1304
- Publication Date
- 1999/07/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
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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 -
English
