IEICE TRANSACTIONS on Information
Region-Based Distributed Estimation Using Quantized Data
Summary :
In this paper, we consider distributed estimation where the measurement at each of the distributed sensor nodes is quantized before being transmitted to a fusion node which produces an estimate of the parameter of interest. Since each quantized measurement can be linked to a region where the parameter is found, aggregating the information obtained from multiple nodes corresponds to generating intersections between the regions. Thus, we develop estimation algorithms that seek to find the intersection region with the maximum likelihood rather than the parameter itself. Specifically, we propose two practical techniques that facilitate fast search with significantly reduced complexity and apply the proposed techniques to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our simulation results show that our proposed algorithms achieve good performance with reasonable complexity as compared with the minimum mean squared error (MMSE) and the maximum likelihood (ML) estimators.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.12 pp.3155-3162
- Publication Date
- 2014/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7063
- Type of Manuscript
- PAPER
- Category
- Information Network
Authors
Yoon Hak KIM
Chosun University
Keyword
Latest Issue
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Yoon Hak KIM, "Region-Based Distributed Estimation Using Quantized Data" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 12, pp. 3155-3162, December 2014, doi: 10.1587/transinf.2014EDP7063.
Abstract: In this paper, we consider distributed estimation where the measurement at each of the distributed sensor nodes is quantized before being transmitted to a fusion node which produces an estimate of the parameter of interest. Since each quantized measurement can be linked to a region where the parameter is found, aggregating the information obtained from multiple nodes corresponds to generating intersections between the regions. Thus, we develop estimation algorithms that seek to find the intersection region with the maximum likelihood rather than the parameter itself. Specifically, we propose two practical techniques that facilitate fast search with significantly reduced complexity and apply the proposed techniques to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our simulation results show that our proposed algorithms achieve good performance with reasonable complexity as compared with the minimum mean squared error (MMSE) and the maximum likelihood (ML) estimators.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7063/_p
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@ARTICLE{e97-d_12_3155,
author={Yoon Hak KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Region-Based Distributed Estimation Using Quantized Data},
year={2014},
volume={E97-D},
number={12},
pages={3155-3162},
abstract={In this paper, we consider distributed estimation where the measurement at each of the distributed sensor nodes is quantized before being transmitted to a fusion node which produces an estimate of the parameter of interest. Since each quantized measurement can be linked to a region where the parameter is found, aggregating the information obtained from multiple nodes corresponds to generating intersections between the regions. Thus, we develop estimation algorithms that seek to find the intersection region with the maximum likelihood rather than the parameter itself. Specifically, we propose two practical techniques that facilitate fast search with significantly reduced complexity and apply the proposed techniques to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our simulation results show that our proposed algorithms achieve good performance with reasonable complexity as compared with the minimum mean squared error (MMSE) and the maximum likelihood (ML) estimators.},
keywords={},
doi={10.1587/transinf.2014EDP7063},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Region-Based Distributed Estimation Using Quantized Data
T2 - IEICE TRANSACTIONS on Information
SP - 3155
EP - 3162
AU - Yoon Hak KIM
PY - 2014
DO - 10.1587/transinf.2014EDP7063
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2014
AB - In this paper, we consider distributed estimation where the measurement at each of the distributed sensor nodes is quantized before being transmitted to a fusion node which produces an estimate of the parameter of interest. Since each quantized measurement can be linked to a region where the parameter is found, aggregating the information obtained from multiple nodes corresponds to generating intersections between the regions. Thus, we develop estimation algorithms that seek to find the intersection region with the maximum likelihood rather than the parameter itself. Specifically, we propose two practical techniques that facilitate fast search with significantly reduced complexity and apply the proposed techniques to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our simulation results show that our proposed algorithms achieve good performance with reasonable complexity as compared with the minimum mean squared error (MMSE) and the maximum likelihood (ML) estimators.
ER -
English
