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Distributed Pareto-optimal state estimation using sensor networks
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0001-9810-3478
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 93, p. 211-223Article in journal (Refereed) Published
Abstract [en]

A novel model-based dynamic distributed state estimator is proposed using sensor networks. The estimator consists of a filtering step – which uses a weighted combination of information provided by the sensors – and a model-based predictor of the system's state. The filtering weights and the model-based prediction parameters jointly minimize – at each time-step – the bias and the variance of the prediction error in a Pareto optimization framework. The simultaneous distributed design of the filtering weights and of the model-based prediction parameters is considered, differently from what is normally done in the literature. It is assumed that the weights of the filtering step are in general unequal for the different state components, unlike existing consensus-based approaches. The state, the measurements, and the noise components are allowed to be individually correlated, but no probability distribution knowledge is assumed for the noise variables. Each sensor can measure only a subset of the state variables. The convergence properties of the mean and of the variance of the prediction error are demonstrated, and they hold both for the global and the local estimation errors at any network node. Simulation results illustrate the performance of the proposed method, obtaining better results than state of the art distributed estimation approaches.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 93, p. 211-223
Keywords [en]
Distributed, Networks, Optimal estimation, Prediction, Sensor, State estimation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-227531DOI: 10.1016/j.automatica.2018.03.071ISI: 000436916200023Scopus ID: 2-s2.0-85044602840OAI: oai:DiVA.org:kth-227531DiVA, id: diva2:1206144
Funder
EU, Horizon 2020, 739551
Note

QC 20180516

Available from: 2018-05-16 Created: 2018-05-16 Last updated: 2018-07-17Bibliographically 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
  • rtf