Multi-sensor scheduling for state estimation with event-based, stochastic triggers

  • Sean Weerakkody
  • , Yilin Mo
  • , Bruno Sinopoli
  • , Duo Han
  • , Ling Shi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

In Networked Control Systems, remote state estimation is hampered by limitations in sensor to estimator communication. Past approaches involving scheduling sensors dynamically via a deterministic event-triggering mechanism reduce communication while maintaining estimation quality. However, these approaches destroy the Gaussian property of the innovation process, making it computationally intractable to obtain an exact minimum mean squared error (MMSE) estimate. Recent work has proposed utilizing a stochastic event-triggered sensor schedule for state estimation. We extend these results to the multi-sensor case, obtaining closed- form expressions for the MMSE estimator and its covariance matrix as well as performance bounds for the system.

Original languageEnglish
Title of host publication4th IFAC Workshop on Distributed Estimation and Control in Networked Systems, NecSys 2013 - Proceedings
PublisherIFAC Secretariat
Pages15-22
Number of pages8
EditionPART 1
ISBN (Print)9783902823557
DOIs
StatePublished - 2013
Event4th IFAC Workshop on Distributed Estimation and Control in Networked Systems, NecSys 2013 - Koblenz, Germany
Duration: Sep 25 2013Sep 26 2013

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
NumberPART 1
Volume4
ISSN (Print)1474-6670

Conference

Conference4th IFAC Workshop on Distributed Estimation and Control in Networked Systems, NecSys 2013
Country/TerritoryGermany
CityKoblenz
Period09/25/1309/26/13

Keywords

  • Event-based systems
  • Gaussian distributions
  • Kalman-lters
  • Sensor selection
  • State estimation

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