IEICE TRANSACTIONS on Fundamentals
Distributed Optimal Estimation with Scalable Communication Cost
Summary :
This paper addresses distributed optimal estimation over wireless sensor networks with scalable communications. For realizing scalable communication, a data-aggregation method is introduced. Since our previously proposed method cannot guarantee the global optimality of each estimator, a modified protocol is proposed. A modification of the proposed method is that weights are introduced in the data aggregation. For selecting the weight values in the data aggregation, a redundant output reduction method with minimum covariance is discussed. Based on the proposed protocol, all estimators can calculate the optimal estimate. Finally, numerical simulations show that the proposed method can realize both the scalability of communication and high accuracy estimation.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E104-A No.11 pp.1470-1476
- Publication Date
- 2021/11/01
- Publicized
- 2021/05/18
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2020KEP0002
- Type of Manuscript
- Special Section PAPER (Special Section on Circuits and Systems)
- Category
Authors
Ryosuke ADACHI
Yamaguchi University
Yuh YAMASHITA
Hokkaido University
Koichi KOBAYASHI
Hokkaido University
Keyword
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Ryosuke ADACHI, Yuh YAMASHITA, Koichi KOBAYASHI, "Distributed Optimal Estimation with Scalable Communication Cost" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 11, pp. 1470-1476, November 2021, doi: 10.1587/transfun.2020KEP0002.
Abstract: This paper addresses distributed optimal estimation over wireless sensor networks with scalable communications. For realizing scalable communication, a data-aggregation method is introduced. Since our previously proposed method cannot guarantee the global optimality of each estimator, a modified protocol is proposed. A modification of the proposed method is that weights are introduced in the data aggregation. For selecting the weight values in the data aggregation, a redundant output reduction method with minimum covariance is discussed. Based on the proposed protocol, all estimators can calculate the optimal estimate. Finally, numerical simulations show that the proposed method can realize both the scalability of communication and high accuracy estimation.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020KEP0002/_p
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@ARTICLE{e104-a_11_1470,
author={Ryosuke ADACHI, Yuh YAMASHITA, Koichi KOBAYASHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Distributed Optimal Estimation with Scalable Communication Cost},
year={2021},
volume={E104-A},
number={11},
pages={1470-1476},
abstract={This paper addresses distributed optimal estimation over wireless sensor networks with scalable communications. For realizing scalable communication, a data-aggregation method is introduced. Since our previously proposed method cannot guarantee the global optimality of each estimator, a modified protocol is proposed. A modification of the proposed method is that weights are introduced in the data aggregation. For selecting the weight values in the data aggregation, a redundant output reduction method with minimum covariance is discussed. Based on the proposed protocol, all estimators can calculate the optimal estimate. Finally, numerical simulations show that the proposed method can realize both the scalability of communication and high accuracy estimation.},
keywords={},
doi={10.1587/transfun.2020KEP0002},
ISSN={1745-1337},
month={November},}
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TY - JOUR
TI - Distributed Optimal Estimation with Scalable Communication Cost
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1470
EP - 1476
AU - Ryosuke ADACHI
AU - Yuh YAMASHITA
AU - Koichi KOBAYASHI
PY - 2021
DO - 10.1587/transfun.2020KEP0002
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2021
AB - This paper addresses distributed optimal estimation over wireless sensor networks with scalable communications. For realizing scalable communication, a data-aggregation method is introduced. Since our previously proposed method cannot guarantee the global optimality of each estimator, a modified protocol is proposed. A modification of the proposed method is that weights are introduced in the data aggregation. For selecting the weight values in the data aggregation, a redundant output reduction method with minimum covariance is discussed. Based on the proposed protocol, all estimators can calculate the optimal estimate. Finally, numerical simulations show that the proposed method can realize both the scalability of communication and high accuracy estimation.
ER -
English
