Adaptive Estimation in Linear Systems with Unknown Markovian Noise Statistics

Jitendra K. Tugnait*, Abraham H. Haddad, Abraham H. Haddad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


The asymptotic behavior of a Bayes optimal adaptive estimation scheme for a linear discrete-time dynamical system with unknown Markovian noise statistics is investigated. Noise influencing the state equation and the measurement equation is assumed to come from a group of Gaussian distributions having different means and covariances, with transitions from one noise source to another determined by a Markov transition matrix. The transition probability matrix is unknown and can take values only from a finite set An example is simulated to illustrate the convergence.

Original languageEnglish (US)
Pages (from-to)66-78
Number of pages13
JournalIEEE Transactions on Information Theory
Issue number1
StatePublished - Jan 1980

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


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