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
Os problemas de filtragem linear de mínimos quadrados e suavização de ponto fixo de sinais observados de forma incerta são considerados quando o sinal e o ruído aditivo de observação são correlacionados em qualquer tempo de amostragem. Algoritmos recursivos, baseados em uma abordagem de inovação, são propostos sem exigir o conhecimento do modelo de espaço de estados gerador do sinal, mas apenas as funções de autocovariância e covariância cruzada do sinal e do ruído branco de observação, bem como a probabilidade de o sinal existir nas observações.
Seiichi NAKAMORI
Raquel CABALLERO-AGUILA
Aurora HERMOSO-CARAZO
Jose D. JIMENEZ-LOPEZ
Josefa LINARES-PEREZ
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Seiichi NAKAMORI, Raquel CABALLERO-AGUILA, Aurora HERMOSO-CARAZO, Jose D. JIMENEZ-LOPEZ, Josefa LINARES-PEREZ, "Recursive Estimation Algorithm Based on Covariances for Uncertainly Observed Signals Correlated with Noise" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 7, pp. 1706-1712, July 2008, doi: 10.1093/ietfec/e91-a.7.1706.
Abstract: The least-squares linear filtering and fixed-point smoothing problems of uncertainly observed signals are considered when the signal and the observation additive noise are correlated at any sampling time. Recursive algorithms, based on an innovation approach, are proposed without requiring the knowledge of the state-space model generating the signal, but only the autocovariance and crosscovariance functions of the signal and the observation white noise, as well as the probability that the signal exists in the observations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.7.1706/_p
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@ARTICLE{e91-a_7_1706,
author={Seiichi NAKAMORI, Raquel CABALLERO-AGUILA, Aurora HERMOSO-CARAZO, Jose D. JIMENEZ-LOPEZ, Josefa LINARES-PEREZ, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Recursive Estimation Algorithm Based on Covariances for Uncertainly Observed Signals Correlated with Noise},
year={2008},
volume={E91-A},
number={7},
pages={1706-1712},
abstract={The least-squares linear filtering and fixed-point smoothing problems of uncertainly observed signals are considered when the signal and the observation additive noise are correlated at any sampling time. Recursive algorithms, based on an innovation approach, are proposed without requiring the knowledge of the state-space model generating the signal, but only the autocovariance and crosscovariance functions of the signal and the observation white noise, as well as the probability that the signal exists in the observations.},
keywords={},
doi={10.1093/ietfec/e91-a.7.1706},
ISSN={1745-1337},
month={July},}
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TY - JOUR
TI - Recursive Estimation Algorithm Based on Covariances for Uncertainly Observed Signals Correlated with Noise
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1706
EP - 1712
AU - Seiichi NAKAMORI
AU - Raquel CABALLERO-AGUILA
AU - Aurora HERMOSO-CARAZO
AU - Jose D. JIMENEZ-LOPEZ
AU - Josefa LINARES-PEREZ
PY - 2008
DO - 10.1093/ietfec/e91-a.7.1706
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2008
AB - The least-squares linear filtering and fixed-point smoothing problems of uncertainly observed signals are considered when the signal and the observation additive noise are correlated at any sampling time. Recursive algorithms, based on an innovation approach, are proposed without requiring the knowledge of the state-space model generating the signal, but only the autocovariance and crosscovariance functions of the signal and the observation white noise, as well as the probability that the signal exists in the observations.
ER -