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
Este artigo aborda um filtro distribuído em redes de sensores sem fio para estimativa ideal. Um filtro distribuído pelas redes permite que todos os estimadores locais calculem estimativas ótimas com um custo de comunicação escalável. As saídas do filtro distribuído para a estimativa ótima podem ser denotadas como uma solução de um problema de otimização de consenso. Assim, o filtro distribuído é projetado com base no método de multiplicadores de direção alternada distribuída (ADMM). Os pontos notáveis do filtro distribuído baseado no ADMM são: o filtro distribuído tem uma taxa de convergência mais rápida do que o algoritmo de projeção de subgradiente distribuído; o peso, que é otimizado por um problema de programação semidefinido, acelera a taxa de convergência do método proposto.
Ryosuke ADACHI
Yamaguchi University
Yuji WAKASA
Yamaguchi University
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Ryosuke ADACHI, Yuji WAKASA, "Distributed Filter Using ADMM for Optimal Estimation Over Wireless Sensor Network" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 11, pp. 1458-1465, November 2022, doi: 10.1587/transfun.2021KEP0008.
Abstract: This paper addresses a distributed filter over wireless sensor networks for optimal estimation. A distributed filter over the networks allows all local estimators to calculate optimal estimates with a scalable communication cost. Outputs of the distributed filter for the optimal estimation can be denoted as a solution of a consensus optimization problem. Thus, the distributed filter is designed based on distributed alternating direction method of multipliers (ADMM). The remarkable points of the distributed filter based on the ADMM are that: the distributed filter has a faster convergence rate than distributed subgradient projection algorithm; the weight, which is optimized by a semidefinite programming problem, accelerates the convergence rate of the proposed method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021KEP0008/_p
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@ARTICLE{e105-a_11_1458,
author={Ryosuke ADACHI, Yuji WAKASA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Distributed Filter Using ADMM for Optimal Estimation Over Wireless Sensor Network},
year={2022},
volume={E105-A},
number={11},
pages={1458-1465},
abstract={This paper addresses a distributed filter over wireless sensor networks for optimal estimation. A distributed filter over the networks allows all local estimators to calculate optimal estimates with a scalable communication cost. Outputs of the distributed filter for the optimal estimation can be denoted as a solution of a consensus optimization problem. Thus, the distributed filter is designed based on distributed alternating direction method of multipliers (ADMM). The remarkable points of the distributed filter based on the ADMM are that: the distributed filter has a faster convergence rate than distributed subgradient projection algorithm; the weight, which is optimized by a semidefinite programming problem, accelerates the convergence rate of the proposed method.},
keywords={},
doi={10.1587/transfun.2021KEP0008},
ISSN={1745-1337},
month={November},}
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TY - JOUR
TI - Distributed Filter Using ADMM for Optimal Estimation Over Wireless Sensor Network
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1458
EP - 1465
AU - Ryosuke ADACHI
AU - Yuji WAKASA
PY - 2022
DO - 10.1587/transfun.2021KEP0008
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2022
AB - This paper addresses a distributed filter over wireless sensor networks for optimal estimation. A distributed filter over the networks allows all local estimators to calculate optimal estimates with a scalable communication cost. Outputs of the distributed filter for the optimal estimation can be denoted as a solution of a consensus optimization problem. Thus, the distributed filter is designed based on distributed alternating direction method of multipliers (ADMM). The remarkable points of the distributed filter based on the ADMM are that: the distributed filter has a faster convergence rate than distributed subgradient projection algorithm; the weight, which is optimized by a semidefinite programming problem, accelerates the convergence rate of the proposed method.
ER -