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
A identificação cega multicanal é útil em muitas aplicações. Embora muitas abordagens tenham sido propostas para resolver este problema desafiador, os métodos baseados em filtragem adaptativa são atraentes devido à sua eficiência computacional e boa propriedade de convergência. O algoritmo de mínimos quadrados médios normalizados multicanal (MCNLMS) é fácil de implementar, mas converge muito lentamente para uma entrada correlacionada. O algoritmo de projeção afim multicanal (MCAPA) é assim proposto para acelerar a convergência. Contudo, a convergência do MCNLMS e do MCAPA ainda é insatisfatória na prática. Neste artigo, propomos uma abordagem de filtragem de Kalman no domínio do tempo para o problema de identificação multicanal cega. Especificamente, o filtro de Kalman adaptativo proposto é baseado no método de relação cruzada e também utiliza mais vetores de entrada passados para explorar a propriedade de decorrelação. Os resultados da simulação indicam que o método proposto supera significativamente o MCNLMS e o MCAPA em termos de convergência inicial e capacidade de rastreamento.
Yuanlei QI
Institute of Acoustics, Chinese Academy of Sciences,University of Chinese Academy of Sciences
Feiran YANG
Institute of Acoustics, Chinese Academy of Sciences
Ming WU
Institute of Acoustics, Chinese Academy of Sciences,University of Chinese Academy of Sciences
Jun YANG
Institute of Acoustics, Chinese Academy of Sciences,University of Chinese Academy of Sciences
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Yuanlei QI, Feiran YANG, Ming WU, Jun YANG, "A Broadband Kalman Filtering Approach to Blind Multichannel Identification" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 6, pp. 788-795, June 2019, doi: 10.1587/transfun.E102.A.788.
Abstract: The blind multichannel identification is useful in many applications. Although many approaches have been proposed to address this challenging problem, the adaptive filtering-based methods are attractive due to their computational efficiency and good convergence property. The multichannel normalized least mean-square (MCNLMS) algorithm is easy to implement, but it converges very slowly for a correlated input. The multichannel affine projection algorithm (MCAPA) is thus proposed to speed up the convergence. However, the convergence of the MCNLMS and MCAPA is still unsatisfactory in practice. In this paper, we propose a time-domain Kalman filtering approach to the blind multichannel identification problem. Specifically, the proposed adaptive Kalman filter is based on the cross relation method and also uses more past input vectors to explore the decorrelation property. Simulation results indicate that the proposed method outperforms the MCNLMS and MCAPA significantly in terms of the initial convergence and tracking capability.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.788/_p
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@ARTICLE{e102-a_6_788,
author={Yuanlei QI, Feiran YANG, Ming WU, Jun YANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Broadband Kalman Filtering Approach to Blind Multichannel Identification},
year={2019},
volume={E102-A},
number={6},
pages={788-795},
abstract={The blind multichannel identification is useful in many applications. Although many approaches have been proposed to address this challenging problem, the adaptive filtering-based methods are attractive due to their computational efficiency and good convergence property. The multichannel normalized least mean-square (MCNLMS) algorithm is easy to implement, but it converges very slowly for a correlated input. The multichannel affine projection algorithm (MCAPA) is thus proposed to speed up the convergence. However, the convergence of the MCNLMS and MCAPA is still unsatisfactory in practice. In this paper, we propose a time-domain Kalman filtering approach to the blind multichannel identification problem. Specifically, the proposed adaptive Kalman filter is based on the cross relation method and also uses more past input vectors to explore the decorrelation property. Simulation results indicate that the proposed method outperforms the MCNLMS and MCAPA significantly in terms of the initial convergence and tracking capability.},
keywords={},
doi={10.1587/transfun.E102.A.788},
ISSN={1745-1337},
month={June},}
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TY - JOUR
TI - A Broadband Kalman Filtering Approach to Blind Multichannel Identification
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 788
EP - 795
AU - Yuanlei QI
AU - Feiran YANG
AU - Ming WU
AU - Jun YANG
PY - 2019
DO - 10.1587/transfun.E102.A.788
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2019
AB - The blind multichannel identification is useful in many applications. Although many approaches have been proposed to address this challenging problem, the adaptive filtering-based methods are attractive due to their computational efficiency and good convergence property. The multichannel normalized least mean-square (MCNLMS) algorithm is easy to implement, but it converges very slowly for a correlated input. The multichannel affine projection algorithm (MCAPA) is thus proposed to speed up the convergence. However, the convergence of the MCNLMS and MCAPA is still unsatisfactory in practice. In this paper, we propose a time-domain Kalman filtering approach to the blind multichannel identification problem. Specifically, the proposed adaptive Kalman filter is based on the cross relation method and also uses more past input vectors to explore the decorrelation property. Simulation results indicate that the proposed method outperforms the MCNLMS and MCAPA significantly in terms of the initial convergence and tracking capability.
ER -