Event-based state estimation for time-varying stochastic coupling networks with missing measurements under uncertain occurrence probabilities

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This paper is concerned with the event-triggered state estimation problem for time-varying delayed complex networks with stochastic coupling and missing measurements under uncertain occurrence probabilities. The stochastic coupling and missing measurements are modeled by two set of mutually independent Bernoulli random variables, respectively, where the uncertainties of the occurrence probabilities are characterized. In addition, the event-triggered mechanism is employed to reduce the network burden during the data transmissions. The aim of the paper is to propose a robust state estimation method for addressed dynamics networks such that sufficient conditions are obtained to ensure the existence of an optimized upper bound of the estimation error covariance. Moreover, the monotonicity analysis between the trace of obtained upper bound of the estimation error covariance and the deterministic occurrence probability of the missing measurements is conducted. Finally, a numerical example is used to verify the validity of the proposed robust state estimation strategy.