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In this paper, the variance-constrained H∞ filtering problem is investigated for a class of discrete-time stochastic parameter systems. An event-triggered communication protocol is adopted in order to reduce the communication burden where the measurement information required by the filter is updated only when a certain prescribed event is triggered. The purpose of the addressed filtering problem is...
In this paper, the problem of fault estimation is considered for a class of time-varying systems through a sensor network. The measurements collected via the sensor network are subject to probabilistic data missing. A set of least square estimators are designed for the addressed systems such that the estimation error variance is minimized at each tim step. By solving a set of Riccati-like matrix equations,...
This paper is concerned with the finite-horizon recursive filtering problem for a class of nonlinear time-varying systems with missing measurements. The missing measurements are modeled by a series of mutually independent random variables obeying Bernoulli distributions with possibly different occurrence probabilities. Attention is focused on the design of a recursive filter such that, for the missing...
In this paper, the state estimation problem for a class of nonlinear systems with multiple channels and correlated noises is studied within the same framework of Extended Kalman Filter (EKF). In networked systems, when sensors are distributed in a large spatial area and multiple channels are employed to transfer data from different sensors, parts of the measurements may be lost at different rates...
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