Please use this identifier to cite or link to this item: http://repositorio.ugto.mx/handle/20.500.12059/2125
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dc.rights.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0es_MX
dc.creatorMIGUEL ANGEL VAZQUEZ OLGUINes_MX
dc.date.accessioned2020-07-09T21:35:31Z-
dc.date.available2020-07-09T21:35:31Z-
dc.date.issued2019-09-
dc.identifier.urihttp://repositorio.ugto.mx/handle/20.500.12059/2125-
dc.description.abstractWireless sensor networks (WSNs) is a technology with important developments in recent years. Its incursion in areas such as healthcare, industry and services has been steadily increasing, mainly due to the miniaturization of electronics and the growing acceptance of cyber-physical systems. However, a very important subject of research continues to be the development of estimators with the robustness needed for the harsh conditions associated with the WSNs applications. Moreover, such estimators should comply with the unique characteristics imposed by the WSNs like scalability, energy saving and redundancy, while maintaining a consensus on the network. A very popular algorithm for optimal estimation is the Kalman Filter (KF). Many works have implemented it as a sensor fusion technique in WSNs, due to its optimality. However it has been proven that it can not guarantee the robustness needed in real life implementations. In this work we developed a set of robust estimators based on unbiased finite impulse response (UFIR) filters to address the lack of robustness of the popular KF. The developed filters are adequate to be implemented in WSNs. The algorithms have been tested against similar filters based on KF with simulated and real data, showing better results in terms estimation error reduction, where the smallest improvement was of 1.4 percent in terms of the root mean squared error (RMSE). We even produce accurate results in applications where KF could not be implemented. The developed filters attained better robustness against miss-model errors, unknown statistics and missing measurements.es_MX
dc.language.isoengen
dc.rightsinfo:eu-repo/semantics/openAccesses_MX
dc.subject.classificationCIS- Doctorado en Ingeniería Eléctricaes_MX
dc.titleDesign of unbiased state estimators for WSNs with consensus on measurements and estimates and improved robustnessen
dc.typeinfo:eu-repo/semantics/doctoralThesises_MX
dc.creator.idinfo:eu-repo/dai/mx/cvu/226854es_MX
dc.subject.ctiinfo:eu-repo/classification/cti/7es_MX
dc.subject.keywordsUnbiased state estimatorsen
dc.subject.keywordsWireless sensor networksen
dc.subject.keywordsRobustnessen
dc.subject.keywordsCyber-Physical Systemsen
dc.subject.keywordsEstimatorsen
dc.subject.keywordsKalman Filteren
dc.subject.keywordsRobust estimatorses_MX
dc.subject.keywordsUnbiased finite impulse responseen
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_MX
dc.contributor.oneYURIY SHMALIY-
dc.contributor.twoOSCAR GERARDO IBARRA MANZANOesMX
dc.contributor.idoneinfo:eu-repo/dai/mx/cvu/26159-
dc.contributor.idtwoinfo:eu-repo/dai/mx/cvu/19462-
dc.contributor.roleonedirector-
dc.contributor.roletwodirector-
dc.publisher.universityUniversidad de Guanajuatoes_MX
Appears in Collections:Doctorado en Ingeniería Eléctrica

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