On identification and adaptive estimation for systems with interrupted observations

Authors:
Jitendra K. Tugnait
Affiliations:
Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, U.S.A.
Venue:
Automatica (Journal of IFAC)
Year:
1983

Citing 7
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Brief paper: Random sampling approach to state estimation in switching environments

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1971 kyow symposium paper: Optimal stochastic control for discrete-time linear system with interrupted observations

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Optimal recursive estimation with uncertain observation

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Adaptive estimation in linear systems with unknown Markovian noise statistics

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An addendum and correction to: 'On identification and adaptive estimation for systems with interrupted observations'

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Abstract

A linear discrete-time system with interrupted observations is considered. The interrupted observation mechanism is expressed in terms of a finite-state ergodic Markov chain. The transition probability matrix of the Markov chain and some system parameters may be unknown, but constant, and are assumed to belong to a compact set. A novel scheme, called truncated maximum likelihood estimation, is proposed for consistent estimation of the unknown parameters. Sufficient conditions for strong consistency are investigated. The truncated maximum likelihood procedure is computationally feasible whereas the standard maximum likelihood procedure is not, given large observation records. Finally, using the truncated ML algorithm, a suboptimal adaptive state estimator is proposed and its asymptotic behavior is analyzed.