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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.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E105-A No.11 pp.1458-1465

- Publication Date
- 2022/11/01

- Publicized
- 2022/04/12

- Online ISSN
- 1745-1337

- DOI
- 10.1587/transfun.2021KEP0008

- Type of Manuscript
- Special Section PAPER (Special Section on Circuits and Systems)

- Category

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 -