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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Un estimateur sinusoïdal complexe multiple non linéaire (NMSE) est proposé, comme une version étendue et améliorée avec bruit système de l'estimateur sinusoïdal unique présenté précédemment par l'auteur, pour extraire des sinusoïdes complexes multiples dans un bruit blanc. Cet estimateur est dérivé en appliquant un filtre de Kalman complexe étendu (ECKF) à un modèle sinusoïdal complexe multiple bruité avec représentation d'état, où le modèle devient un système stochastique non linéaire. La preuve de la stabilité est donnée en utilisant une structure du modèle de signal espace-état et des techniques de Lyapunov. De plus, des simulations informatiques démontrent l’efficacité du NMSE sous différents points de vue.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
Kiyoshi NISHIYAMA, "A Nonlinear Multiple Complex Sinusoidal Estimator" in IEICE TRANSACTIONS on Fundamentals,
vol. E83-A, no. 3, pp. 497-506, March 2000, doi: .
Abstract: A nonlinear multiple complex sinusoidal estimator (NMSE) is proposed, as an extended and improved version with system noise of the single sinusoidal estimator previously presented by the author, for extracting multiple complex sinusoids in white noise. This estimator is derived by applying an extended complex Kalman filter (ECKF) to a noisy multiple complex sinusoidal model with state-representation, where the model becomes a nonlinear stochastic system. Proof of the stability is given by using a structure of the state-space signal model and Lyapunov techniques. Also, computer simulations demonstrate the effectiveness of the NMSE from various points of view.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e83-a_3_497/_p
Copier
@ARTICLE{e83-a_3_497,
author={Kiyoshi NISHIYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Nonlinear Multiple Complex Sinusoidal Estimator},
year={2000},
volume={E83-A},
number={3},
pages={497-506},
abstract={A nonlinear multiple complex sinusoidal estimator (NMSE) is proposed, as an extended and improved version with system noise of the single sinusoidal estimator previously presented by the author, for extracting multiple complex sinusoids in white noise. This estimator is derived by applying an extended complex Kalman filter (ECKF) to a noisy multiple complex sinusoidal model with state-representation, where the model becomes a nonlinear stochastic system. Proof of the stability is given by using a structure of the state-space signal model and Lyapunov techniques. Also, computer simulations demonstrate the effectiveness of the NMSE from various points of view.},
keywords={},
doi={},
ISSN={},
month={March},}
Copier
TY - JOUR
TI - A Nonlinear Multiple Complex Sinusoidal Estimator
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 497
EP - 506
AU - Kiyoshi NISHIYAMA
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E83-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2000
AB - A nonlinear multiple complex sinusoidal estimator (NMSE) is proposed, as an extended and improved version with system noise of the single sinusoidal estimator previously presented by the author, for extracting multiple complex sinusoids in white noise. This estimator is derived by applying an extended complex Kalman filter (ECKF) to a noisy multiple complex sinusoidal model with state-representation, where the model becomes a nonlinear stochastic system. Proof of the stability is given by using a structure of the state-space signal model and Lyapunov techniques. Also, computer simulations demonstrate the effectiveness of the NMSE from various points of view.
ER -