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Comparative Analysis of Ellipsoidal Methods for Distributed Data Fusion

Published: 05 January 2018 Publication History

Abstract

Progress in sensor and communication technology changed the paradigm of state estimation and data fusion from centralized architecture to distributed/decentralized architecture. These days, distributed state estimation and data fusion are widely explored in diverse fields of engineering and control due to its superior performance over the centralized one in terms of flexibility, robustness to failure and cost effectiveness in infrastructure and communication. However, distributed architectures are accompanied by their own requirements and limitations, especially, cross-correlation among sensor estimates. Due to double counting and common process noise, it is very difficult to exactly estimate the cross-correlation among sensor estimates. Consequently, fusion methodologies seek for a suboptimal fused mean and covariance of multiple data sources under unknown correlation. In this paper, we present a comparative analysis of the Ellipsoidal Methods for distributed data fusion under unknown correlation. These methods aim to provide a suboptimal fused result without the need of actual cross-correlation. We aim at providing readers with a unifying view out of individual methodologies by presenting a formal analysis of their implications. Several directions for future research are also highlighted.

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IMCOM '18: Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication
January 2018
628 pages
ISBN:9781450363853
DOI:10.1145/3164541
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • SKKU: SUNGKYUNKWAN UNIVERSITY

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 January 2018

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Author Tags

  1. Distributed fusion architecture
  2. Distributed sensor network
  3. Kalman filter
  4. Multisensor data fusion
  5. Unknown correlation

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • National Research Foundation (NRF) of Korea
  • Korea Evaluation Institute of Industrial Technology
  • Korea Evaluation Institute of Industrial Technology (KEIT)

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IMCOM '18

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IMCOM '18 Paper Acceptance Rate 100 of 255 submissions, 39%;
Overall Acceptance Rate 213 of 621 submissions, 34%

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