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
Dans cette lettre, nous proposons un nouveau prédicteur du maximum de vraisemblance en plusieurs étapes avec une structure de réponse impulsionnelle finie (FIR) pour les modèles de signaux dans l'espace et les états à temps discret. Ce prédicteur est appelé prédicteur FIR à maximum de vraisemblance (MLFP). Le MLFP est linéaire avec les sorties finies les plus récentes et ne nécessite pas Un préalable informations sur l’état initial sur un horizon fuyant. Il est démontré que le MLFP proposé possède la propriété d'impartialité et la propriété de mauvais payeur. L'étude de simulation montre que le MLFP proposé est plus robuste aux incertitudes et plus rapide en convergence que le prédicteur de Kalman multi-étapes conventionnel.
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ChoonKi AHN, "New Multi-Step FIR Predictors for State-Space Signal Models" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 4, pp. 1233-1236, April 2009, doi: 10.1587/transfun.E92.A.1233.
Abstract: In this letter, we propose a new multi-step maximum likelihood predictor with a finite impulse response (FIR) structure for discrete-time state-space signal models. This predictor is called a maximum likelihood FIR predictor (MLFP). The MLFP is linear with the most recent finite outputs and does not require a prior initial state information on a receding horizon. It is shown that the proposed MLFP possesses the unbiasedness property and the deadbeat property. Simulation study illustrates that the proposed MLFP is more robust against uncertainties and faster in convergence than the conventional multi-step Kalman predictor.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.1233/_p
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@ARTICLE{e92-a_4_1233,
author={ChoonKi AHN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={New Multi-Step FIR Predictors for State-Space Signal Models},
year={2009},
volume={E92-A},
number={4},
pages={1233-1236},
abstract={In this letter, we propose a new multi-step maximum likelihood predictor with a finite impulse response (FIR) structure for discrete-time state-space signal models. This predictor is called a maximum likelihood FIR predictor (MLFP). The MLFP is linear with the most recent finite outputs and does not require a prior initial state information on a receding horizon. It is shown that the proposed MLFP possesses the unbiasedness property and the deadbeat property. Simulation study illustrates that the proposed MLFP is more robust against uncertainties and faster in convergence than the conventional multi-step Kalman predictor.},
keywords={},
doi={10.1587/transfun.E92.A.1233},
ISSN={1745-1337},
month={April},}
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TY - JOUR
TI - New Multi-Step FIR Predictors for State-Space Signal Models
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1233
EP - 1236
AU - ChoonKi AHN
PY - 2009
DO - 10.1587/transfun.E92.A.1233
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2009
AB - In this letter, we propose a new multi-step maximum likelihood predictor with a finite impulse response (FIR) structure for discrete-time state-space signal models. This predictor is called a maximum likelihood FIR predictor (MLFP). The MLFP is linear with the most recent finite outputs and does not require a prior initial state information on a receding horizon. It is shown that the proposed MLFP possesses the unbiasedness property and the deadbeat property. Simulation study illustrates that the proposed MLFP is more robust against uncertainties and faster in convergence than the conventional multi-step Kalman predictor.
ER -