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dc.contributor.authorTalebi, Sayedpouria
dc.contributor.authorWerner, Stefan
dc.contributor.authorGupta, Vijay
dc.contributor.authorHuang, Yih-Fang
dc.date.accessioned2022-03-10T11:30:21Z
dc.date.available2022-03-10T11:30:21Z
dc.date.created2021-09-03T13:44:11Z
dc.date.issued2021
dc.identifier.citationIEEE Signal Processing Letters. 2021, 28 494-498.en_US
dc.identifier.issn1070-9908
dc.identifier.urihttps://hdl.handle.net/11250/2984233
dc.description.abstractRecent years have bore witness to the proliferation of distributed filtering techniques, where a collection of agents communicating over an ad-hoc network aim to collaboratively estimate and track the state of a system. These techniques form the enabling technology of modern multi-agent systems and have gained great importance in the engineering community. Although most distributed filtering techniques come with a set of stability and convergence criteria, the conditions imposed are found to be unnecessarily restrictive. The paradigm of stability and convergence in distributed filtering is revised in this manuscript. Accordingly, a general distributed filter is constructed and its estimation error dynamics is formulated. The conducted analysis demonstrates that conditions for achieving stable filtering operations are the same as those required in the centralized filtering setting. Finally, the concepts are demonstrated in a Kalman filtering framework and validated using simulation examples.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.titleOn Stability and Convergence of Distributed Filtersen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 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.en_US
dc.source.pagenumber494-498en_US
dc.source.volume28en_US
dc.source.journalIEEE Signal Processing Lettersen_US
dc.identifier.doi10.1109/LSP.2021.3059207
dc.identifier.cristin1931171
dc.relation.projectNorges forskningsråd: 300102en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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