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In this paper, adaptive filtering schemes are proposed for state estimation in sensor networks and/or networked control systems with mixed uncertainties of random measurement delays, packet dropouts and missing measurements. That is, all three uncertainties in the measurement have certain probability of occurrence in the network. The filter gains can be derived by solving a set of recursive discrete-time...
In this paper, an H?? filter design problem is studied for networked stochastic linear discrete-time systems with mixed uncertainties of random measurement delays, packet dropouts and missing measurements. We model the packet delivery characteristics of the network as a four-state Markov process and note that the results for discrete-time linear systems with Markovian jumping parameters can be applied...
In this paper an optimal Kalman filter design problem is studied for networked stochastic linear discrete-time systems with random measurement delays, packet dropouts and missing measurements. Any of these three uncertainties in the measurement can occur in the network in the same run. Based on a Markov chain, we develop a unified/combined model to accommodate random delay, packet dropouts and missing...
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