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• W2105956919 abstract "This paper deals with the distributed state estimation problem for a class of sensor networks described by discrete-time stochastic systems with randomly varying nonlinearities and missing measurements. In the sensor network, there is no centralized processor capable of collecting all the measurements from the sensors, and therefore each individual sensor needs to estimate the system state based not only on its own measurement but also on its neighboring sensors' measurements according to certain topology. The stochastic Brownian motions affect both the dynamical plant and the sensor measurement outputs. The randomly varying nonlinearities and missing measurements are introduced to reflect more realistic dynamical behaviors of the sensor networks that are caused by noisy environment as well as by probabilistic communication failures. Through available output measurements from each individual sensor, we aim to design distributed state estimators to approximate the states of the networked dynamic system. Sufficient conditions are presented to guarantee the convergence of the estimation error systems for all admissible stochastic disturbances, randomly varying nonlinearities, and missing measurements. Then, the explicit expressions of individual estimators are derived to facilitate the distributed computing of state estimation from each sensor. Finally, a numerical example is given to verify the theoretical results." @default.
• W2105956919 created "2016-06-24" @default.
• W2105956919 creator A5035358476 @default.
• W2105956919 creator A5048831651 @default.
• W2105956919 creator A5091673337 @default.
• W2105956919 date "2011-03-01" @default.
• W2105956919 modified "2023-09-23" @default.
• W2105956919 title "Distributed State Estimation for Discrete-Time Sensor Networks With Randomly Varying Nonlinearities and Missing Measurements" @default.
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• W2105956919 doi "https://doi.org/10.1109/tnn.2011.2105501" @default.
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