Continuous-time decentralized smoothers based on two-filter form: Identical local and global models

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Abstract

This paper proposes some decentralized smoothing algorithms for a continuous-time linear estimation structure consisting of a central processor and of two local processors, in which the local models are assumed to be identical to the global model. The philosophy of the paper is to solve the problem in terms of the local forward and backward information (or Kalman) filters. The resulting algorithms are somewhat different from those based on the local smoothing estimates which have been studied by some other authors. Smoothing update and real-time smoothing algorithms are also presented, ft is shown that the present algorithms have some advantages: the global filtered estimates can be obtained in the course of computing the decentralized smoothing estimates and the central and local processors can be derived in a completely parallel fashion.

Original languageEnglish
Pages (from-to)1015-1028
Number of pages14
JournalInternational Journal of Systems Science
Volume17
Issue number7
DOIs
Publication statusPublished - Jul 1986
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications

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