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DC Field | Value | Language |
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dc.contributor.author | Huang, Y-B | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | He, Y | - |
dc.contributor.author | Wu, M | - |
dc.date.accessioned | 2024-04-11T20:14:59Z | - |
dc.date.available | 2024-04-11T20:14:59Z | - |
dc.date.issued | 2024-03-19 | - |
dc.identifier | ORCiD: Yi-Bo Huang https://orcid.org/0000-0002-9163-6850 | - |
dc.identifier | ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401 | - |
dc.identifier | ORCiD: Yong He https://orcid.org/0000-0001-5691-9663 | - |
dc.identifier | ORCiD: Min Wu https://orcid.org/0000-0002-0668-8315 | - |
dc.identifier.citation | Huang, Y.-B. et al. (2024) 'Recursive State Estimation for Discrete-Time Nonlinear Systems With Binary Sensors: A Locally Minimized Variance Approach', IEEE Transactions on Automatic Control, 0 (early access), pp. 1 - 8. doi: 10.1109/TAC.2024.3378776. | en_US |
dc.identifier.issn | 0018-9286 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28753 | - |
dc.description.abstract | In this paper, the locally-minimized-variance state estimation problem is investigated for a class of discrete-time nonlinear systems with Lipschitz nonlinearities and binary sensors. The output of each binary sensor takes two possible values (e.g. 0 and 1) in accordance with whether the sensed variable surpasses a prescribed threshold or not. The purpose of this paper is to design a state estimation algorithm such that an upper bound of the estimation error covariance is firstly guaranteed and then minimized at each sampling instant by properly designing the estimator gain. The valid information of sensed variables is extracted from binary measurements, and a novel state estimator is constructed in a recursive form, which is suitable for online computations. Moreover, a sufficient condition is established to ensure the exponential boundedness of the prediction error in the mean square sense. Finally, two examples are presented to verify the effectiveness of the proposed method. | en_US |
dc.description.sponsorship | National Natural Science Foundation of China (Grant Number: 61933007 and 62373333); Royal Society of the U.K.;Alexander von Humboldt Foundation of Germany. | en_US |
dc.format.extent | 1 - 8 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2024 Institute of Electrical and Electronics Engineers (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 by sending a request to pubs-permissions@ieee.org. See https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ for more information. | - |
dc.rights.uri | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.subject | binary sensors | en_US |
dc.subject | difference equations | en_US |
dc.subject | nonlinear systems | en_US |
dc.subject | recursive state estimation | en_US |
dc.subject | variance constraints | en_US |
dc.title | Recursive State Estimation for Discrete-Time Nonlinear Systems With Binary Sensors: A Locally Minimized Variance Approach | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/TAC.2024.3378776 | - |
dc.relation.isPartOf | IEEE Transactions on Automatic Control | - |
pubs.issue | early access | - |
pubs.publication-status | Published | - |
pubs.volume | 0 | - |
dc.identifier.eissn | 1558-2523 | - |
dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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