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This paper is concerned with the Tobit Kalman filtering for a class of discrete-time linear systems. A set of Bernoulli random variables is introduced to describe the randomly occurring censored measurements, which are dependent on the measurement outputs. Such dependence among random variables leads to the largest challenge encountered in this paper. The conditional expectation as a basic tool is...
This paper is concerned with the distributed state estimation problem for a class of time-varying systems over sensor networks. An event-triggered communication scheme is utilized to save the constrained computation resource and network bandwidth while preserving the desired performance. The measurements on each node are transmitted to the estimators only when a certain triggering condition is satisfied...
This paper is concerned with the distributed fault estimation problem for a class of time-varying systems which are subject to randomly occurring nonlinearities (RONs) over sensor networks. The random nonlinearities are characterized by random variables obeying certain probabilistic distributions in the interval [0, 1]. And the available output measurements are obtained from both the individual sensor...
In this paper, the state estimation problem is investigated for a class of discrete uncertain neural networks subject to time delays and missing measurements. Several mutually independent sets of Bernoulli-distributed white sequences are employed to describe the phenomena of randomly occurring uncertainties (ROUs) and missing measurements. We aim to design a state estimator such that, in the presence...
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