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Optimal pseudo-steady-state estimators for systems with Markovian intermittent measurements

A state estimator design is described for discrete time systems having observably intermittent measurements. A stationary Markov process is used to model probabilistic measurement losses. The stationarity of the Markov process suggests an analogous stationary estimator design related to the Markov states. A precomputable time-varying state estimator is proposed as an alternative to Kalman's optimal time-varying estimation scheme applied to a discrete linear system with Markovian intermittent measurements. An iterative scheme to find optimal precomputed estimators is given. The results here naturally extend to Markovian jump linear systems.