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A distributed estimation method for sensor networks based on Pareto optimization
Department of Industrial and Information Engineering, University of Trieste.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9810-3478
Department of Industrial and Information Engineering, University of Trieste.
2012 (English)In: Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, IEEE , 2012, 775-781 p.Conference paper, Published paper (Refereed)
Abstract [en]

A novel distributed estimation method for sensor networks is proposed. The goal is to track a time-varying signal that is jointly measured by a network of sensor nodes despite the presence of noise: each node computes its local estimate as a weighted sum of its own and its neighbors' measurements and estimates and updates its weights to minimize both the variance and the mean of the estimation error by means of a suitable Pareto optimization problem. The estimator does not rely on a central coordination: both parameter optimization and estimation are distributed across the nodes. The performance of the distributed estimator is investigated in terms of estimation bias and estimation error. Moreover, an upper bound of the bias is provided. The effectiveness of the proposed estimator is illustrated via computer simulations and the performances are compared with other distributed schemes previously proposed in the literature. The results show that the estimation quality is comparable to that of one of the best existing distributed estimation algorithms, guaranteeing lower computational cost and time.

Place, publisher, year, edition, pages
IEEE , 2012. 775-781 p.
Series
IEEE Conference on Decision and Control. Proceedings, ISSN 0191-2216
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-118791DOI: 10.1109/CDC.2012.6426731Scopus ID: 2-s2.0-84874275025OAI: oai:DiVA.org:kth-118791DiVA: diva2:608392
Conference
51st IEEE Conference on Decision and Control, CDC 2012; Maui, HI;10 December 2012 through 13 December 2012
Note

QC 20130304

Available from: 2013-02-27 Created: 2013-02-27 Last updated: 2013-03-04Bibliographically approved

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Fischione, Carlo

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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