NDLI: Distributed State Estimation for Discrete-Time Sensor Networks With Randomly Varying Nonlinearities and Missing Measurements

Content Provider	IEEE Xplore Digital Library
Author	Jinling Liang Zidong Wang Xiaohui Liu
Copyright Year	1990
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.
Sponsorship	IEEE Computational Intelligence Society
Page Count	11
File Size	372313
Starting Page	486
Ending Page	496
File Format	PDF
ISSN	10459227
Volume Number	22
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes State estimation Symmetric matrices Complex networks Noise measurement Probabilistic logic stochastic disturbances Distributed state estimation missing measurements randomly varying nonlinearity sensor network
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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