Linear Fusion under Random Correlation of Estimation Errors

dc.contributor.authorAjgl, Jiří
dc.contributor.authorStraka, Ondřej
dc.date.accessioned2023-02-13T11:00:18Z
dc.date.available2023-02-13T11:00:18Z
dc.date.issued2022
dc.description.abstract-translatedLinear fusion of estimates has been studied from the perspectives of known and unknown correlations of estimation errors. Whereas optimal linear combinations can be designed in the former case, a robust approach is usually chosen in the latter one. The loss of performance may be unacceptably high, which raises the need to find a middle ground. This paper reviews various approaches to information fusion, formulates the problem of random correlation and presents the solution. Monte Carlo verification of the results is discussed and an illustration is provided.en
dc.format5 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationAJGL, J. STRAKA, O. Linear Fusion under Random Correlation of Estimation Errors. In Proceedings of the 30th European Signal Processing Conference (EUSIPCO 2022). Bělehrad, Srbsko: IEEE, 2022. s. 2176-2180. ISBN: 978-90-827970-9-1 , ISSN: 2219-5491cs
dc.identifier.doi10.23919/EUSIPCO55093.2022.9909868
dc.identifier.isbn978-90-827970-9-1
dc.identifier.issn2219-5491
dc.identifier.obd43936814
dc.identifier.uri2-s2.0-85141010470
dc.identifier.urihttp://hdl.handle.net/11025/51444
dc.language.isoenen
dc.project.IDGC20-06054J/Inteligentní distribuované architektury pro odhad stavucs
dc.project.IDSGS-2022-022/Rozvoj a využití kybernetických systémů identifikace, diagnostiky a řízení 5cs
dc.publisherIEEEen
dc.relation.ispartofseriesProceedings of the 30th European Signal Processing Conference (EUSIPCO 2022)en
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© IEEEen
dc.rights.accessrestrictedAccessen
dc.subject.translatedstochastic systems, linear estimation, information fusion, unknown correlation, random correlationen
dc.titleLinear Fusion under Random Correlation of Estimation Errorsen
dc.typekonferenční příspěvekcs
dc.typeConferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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