Adaptive estimation in linear systems with unknown Markovian noise statistics

Authors:
J. Tugnait;A. Haddad
Affiliations:
-;-
Venue:
IEEE Transactions on Information Theory
Year:
2006

Citing 0
Cited 3

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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.