Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142739
Title: A new approach to linear/nonlinear distributed fusion estimation problem
Authors: Chen, Bo
Hu, Guoqiang
Ho, Daniel W. C.
Yu, Li
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Chen, B., Hu, G., Ho, D. W. C., & Yu, L. (2019). A new approach to linear/nonlinear distributed fusion estimation problem. IEEE Transactions on Automatic Control, 64(3), 1301-1308. doi:10.1109/TAC.2018.2849612
Journal: IEEE Transactions on Automatic Control
Abstract: In this paper, we study the distributed fusion estimation problem for linear time-varying systems and nonlinear systems with bounded noises, where the addressed noises do not provide any statistical information, and are unknown but bounded. When considering linear time-varying fusion systems with bounded noises, a new local Kalman-like estimator is designed such that the square error of the estimator is bounded as time goes to ∞. A novel constructive method is proposed to find an upper bound of fusion estimation error, then a convex optimization problem on the design of an optimal weighting fusion criterion is established in terms of linear matrix inequalities, which can be solved by standard software packages. Furthermore, according to the design method of linear time-varying fusion systems, each local nonlinear estimator is derived for nonlinear systems with bounded noises by using Taylor series expansion, and a corresponding distributed fusion criterion is obtained by solving a convex optimization problem. Finally, target tracking system and localization of a mobile robot are given to show the advantages and effectiveness of the proposed methods.
URI: https://hdl.handle.net/10356/142739
ISSN: 0018-9286
DOI: 10.1109/TAC.2018.2849612
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TAC.2018.2849612.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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