Abstract
In this paper the Kalman filtering problem for networked stochastic linear discrete-time systems with random measurement delays, packet dropouts and missing measurements is studied. Based on a quasi Markov-chain approach, a unified/combined model is developed to accommodate random delay, packet dropout and missing measurement. Two approaches for constructing a filter via the linear matrix inequality approach are proposed. Simulation studies are then conducted to evaluate the effectiveness of the constructed estimators.
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