The fusion of redundant SEVA measurements

M. Duta, M. Henry

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

The self-validating (SEVA) sensor carries out an internal quality assessment, and generates, for each measurement, standard metrics for its quality, including online uncertainty. This paper discusses consistency checking and data fusion between several SEVA sensors observing the same measurand. Consistency checking is shown to be equivalent to the maximum clique problem, which is NP-hard, but a linear approximation is described. A technique called uncertainty extension is proposed which causes a smooth reduction in the influence of outliers as they become increasingly inconsistent with the majority.
Original languageEnglish
Pages (from-to)173 - 184
Number of pages12
JournalIEEE Transactions on Control Systems Technology
Volume13
Issue number2
DOIs
Publication statusPublished - 28 Feb 2005
Externally publishedYes

Keywords

  • Maximum clique problem
  • self-validating (SEVA) sensors
  • sensor fusion

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