2019
DOI: 10.1002/asjc.2051
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Event‐triggered robust state estimation for wireless sensor networks

Abstract: Robust state estimation problem subject to a communication constraint is investigated in this paper for a class of wireless sensor networks constituted by multiple remote sensor nodes and a fusion node. An analytical robust fusion estimator using local event‐triggered transmission strategies is derived aiming to reduce energy consumption of the sensor nodes and refrain from network traffic congestion. Some conditions are presented guaranteeing the uniformly bounded estimation errors of the robust state estimat… Show more

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Cited by 8 publications
(5 citation statements)
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References 35 publications
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“…(c) Appropriately increasing the hidden neurons helps to improve the model performance, but excessive increase will lead to overfitting. The investigated method also can be adopted to estimate the remaining useful life of supercapacitors, 39,40 can be applied to network dynamic system modeling and identification, 41,42 and can be used in block‐oriented systems with NN nonlinear parts 43‐45 …”
Section: Discussionmentioning
confidence: 99%
“…(c) Appropriately increasing the hidden neurons helps to improve the model performance, but excessive increase will lead to overfitting. The investigated method also can be adopted to estimate the remaining useful life of supercapacitors, 39,40 can be applied to network dynamic system modeling and identification, 41,42 and can be used in block‐oriented systems with NN nonlinear parts 43‐45 …”
Section: Discussionmentioning
confidence: 99%
“…Consequently, numerous estimation algorithms have been formulated, encompassing the likes of the Kalman filter, Wiener filter, and other notable methodologies. In the system modeling process, modelling errors are inevitable, so the estimator performance must have no sudden changes when the system parameters reasonably deviate from their nominal parameters [26]. Those with this property are called robust state estimators, and many research methods are available [27][28][29][30].…”
Section: Introductionmentioning
confidence: 99%
“…Sensor networks are composed of a large number of intelligent sensor nodes, which are distributed in specific areas. During the past decades, sensor networks have given rise to more and more research interests owing to their extensive applications, such as environmental monitoring, industrial automation, and health care systems [11][12][13][14]. Recently, the distributed filtering issues have been reflected over sensor networks.…”
Section: Introductionmentioning
confidence: 99%