Decentralized multiple model adaptive filtering for discrete-time stochastic systems

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2 Citations (Scopus)

Abstract

A decentralized multiple model adaptive filter (MMAF) is proposed for linear discrete-time stochastic systems. The structure of decentralized multiple model studied here is based on introducing a global hypothesis for the global model and a local hypothesis for the local model, where it is assumed that the former hypothesis includes the latter one as a partial element. Algorithms for the decentralized MMAFs in unsteady and steady-state are derived using recent results in decentralized Kalman filtering. The results can be applied in designing a system for sensor failure detection and identification (FDI).

Original languageEnglish
Pages (from-to)371-377
Number of pages7
JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
Volume111
Issue number3
Publication statusPublished - Sep 1989
Externally publishedYes

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Adaptive filtering
Stochastic systems
unsteady state
adaptive filters
Adaptive filters
sensors
Sensors

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Instrumentation

Cite this

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abstract = "A decentralized multiple model adaptive filter (MMAF) is proposed for linear discrete-time stochastic systems. The structure of decentralized multiple model studied here is based on introducing a global hypothesis for the global model and a local hypothesis for the local model, where it is assumed that the former hypothesis includes the latter one as a partial element. Algorithms for the decentralized MMAFs in unsteady and steady-state are derived using recent results in decentralized Kalman filtering. The results can be applied in designing a system for sensor failure detection and identification (FDI).",
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