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DC Field | Value | Language |
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dc.contributor.author | Li, Q | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Dong, H | - |
dc.contributor.author | Sheng, W | - |
dc.date.accessioned | 2023-03-03T17:16:08Z | - |
dc.date.available | 2023-03-03T17:16:08Z | - |
dc.date.issued | 2022-10-10 | - |
dc.identifier | ORCID iD: Zidong Wang https://orcid.org/0000-0002-9576-7401 | - |
dc.identifier.citation | Li, Q. et al. (2022) 'Recursive filtering for complex networks with time-correlated fading channels: An outlier-resistant approach', Information Sciences, 615, pp. 348 - 367. doi: 10.1016/j.ins.2022.10.023. | en_US |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26048 | - |
dc.description.abstract | In this paper, the outlier-resistant recursive filtering problem is fully discussed for complex networks with time-correlated fading channels. Each sensor is able to communicate with its corresponding filter within a set of time-correlated fading channels, and the channel coefficient is assumed to be governed by certain dynamical process. In order to alleviate undesired effects (e.g. performance degradation or even divergence of the filtering error) from possible measurement outliers, a certain saturation structure is introduced in our constructed filter. The purpose is at estimating network states with satisfactory error dynamics with not only time-correlated fading channels but also measurement outliers. First, an augmented model is constructed in order to combine network dynamic evolutions along with channel coefficients. Subsequently, by means of the inductive method, upper covariance bounds are first given and later minimized by properly parameterizing filter gains. Finally, two example are given for effectiveness validation. | en_US |
dc.description.sponsorship | This work was supported in part by the National Natural Science Foundation of China under Grants 62003121, U21A2019, 61933007, 61873082, the Zhejiang Provincial Natural Science Foundation of China under Grant LQ20F030014, the Hainan Province Science and Technology Special Fund of China under Grant ZDYF2022SHFZ105, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany. | en_US |
dc.format.extent | 348 - 367 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Copyright © 2022 Elsevier Inc. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.ins.2022.10.023, made available on this repository under a Creative Commons CC BY-NC-ND attribution licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | complex networks | en_US |
dc.subject | recursive filtering | en_US |
dc.subject | time-correlated fading channels | en_US |
dc.subject | measurement outliers | en_US |
dc.title | Recursive filtering for complex networks with time-correlated fading channels: An outlier-resistant approach | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.ins.2022.10.023 | - |
dc.relation.isPartOf | Information Sciences | - |
pubs.publication-status | Published | - |
pubs.volume | 615 | - |
dc.identifier.eissn | 1872-6291 | - |
dc.rights.holder | Elsevier Inc. | - |
Appears in Collections: | Dept of Computer Science Embargoed Research Papers |
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