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Titlebook: Distributed Fusion Estimation for Sensor Networks with Communication Constraints; Wen-An Zhang,Bo Chen,Li Yu Book 2016 Science Press, Beij

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書目名稱Distributed Fusion Estimation for Sensor Networks with Communication Constraints
編輯Wen-An Zhang,Bo Chen,Li Yu
視頻videohttp://file.papertrans.cn/282/281896/281896.mp4
概述The first book to discuss multi-sensor fusion estimation for sensor networks.with communication constraints.Provide a novel dimension reduction method for energy-efficient fusion.Provide a novel multi
圖書封面Titlebook: Distributed Fusion Estimation for Sensor Networks with Communication Constraints;  Wen-An Zhang,Bo Chen,Li Yu Book 2016 Science Press, Beij
描述.Thisbook systematically presents energy-efficient robust fusion estimation methodsto achieve thorough and comprehensive results in the context of network-basedfusion estimation. It summarizes recent findings on fusion estimation withcommunication constraints; several novel energy-efficient and robust designmethods for dealing with energy constraints and network-induced uncertaintiesare presented, such as delays, packet losses, and asynchronous information...All the results are presented as algorithms, which are convenient for practicalapplications..
出版日期Book 2016
關(guān)鍵詞Communication constraints; Information fusion; Kalman filtering; Optimal estimation; Sensor networks
版次1
doihttps://doi.org/10.1007/978-981-10-0795-8
isbn_softcover978-981-10-9254-1
isbn_ebook978-981-10-0795-8
copyrightScience Press, Beijing and Springer Science+Business Media Singapore 2016
The information of publication is updating

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Kalman Fusion Estimation for WSNs with Nonuniform Estimation Rates,sampling instant from the energy-efficiency perspective, and the sensors may work and generate estimates with two rates, namely, a fast rate and a slow rate according to their power situations. Therefore, adopting a nonuniform estimation rate is a more preferable strategy for sensor network-based estimation system with energy constraints.
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Fusion Estimation for WSNs with Delayed Measurements,by using the innovation analysis approach and linear matrix inequality approach [1–7]. For the multisensor case, the information fusion problem has been investigated in [8, 9] for linear stochastic systems with time-delayed measurements, where the observation delays were assumed to be constant.
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