Adaptive Estimation in Linear Systems with Unknown Markovian Noise Statistics

Jitendra K. Tugnait; Abraham H. Haddad; Abraham H. Haddad

doi:10.1109/TIT.1980.1056131

Adaptive Estimation in Linear Systems with Unknown Markovian Noise Statistics

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

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

21 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	66-78
Number of pages	13
Journal	IEEE Transactions on Information Theory
Volume	26
Issue number	1
DOIs	https://doi.org/10.1109/TIT.1980.1056131
State	Published - Jan 1980

ASJC Scopus subject areas

Information Systems
Computer Science Applications
Library and Information Sciences

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10.1109/TIT.1980.1056131

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AB - 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.

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Adaptive Estimation in Linear Systems with Unknown Markovian Noise Statistics

Abstract

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