A Nonlinear Scheme for Parameter Estimation in Linear Systems

R. S. Brownell*, A. H. Haddad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

An algorithm is presented which provides a maximum-likelihood estimate for an unknown parameter contained in a linear dynamic system driven by white, Gaussian noise. Taylor series expansions are used to develop approximations to the estimation equations. These approximations are recursive and can be calculated iteratively. The algorithm can be realized either as an analog or as a digital system and is shown to compare favorably with existing techniques in a simple example.

Original languageEnglish (US)
Pages (from-to)145-153
Number of pages9
JournalJournal of the Franklin Institute
Volume294
Issue number3
DOIs
StatePublished - Sep 1972

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics

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