A linear estimator for network localization using integrated RSS and AOA measurements

dc.contributor.authorTomic, Slavisa
dc.contributor.authorBeko, Marko
dc.contributor.authorTuba, Milan
dc.contributor.institutionCOPELABS (FCT) - Centro de Investigação em Computação Centrada nas Pessoas e Cognição (CTS)
dc.date.issued2019-03
dc.descriptionPublisher Copyright: © 2019 IEEE.
dc.description.abstractThis letter addresses the problem of simultaneous localization of multiple targets in three-dimensional cooperative wireless sensor networks. To this end, integrated received signal strength and angle of arrival measurements are employed. By exploiting the convenient nature of spherical representation of the considered problem, the measurement models are linearized and a sub-optimal estimator is formulated. Unlike the maximum likelihood estimator, which is highly non-convex and difficult to tackle directly, the derived estimator is quadratic and has a closed-form solution. Its computational complexity is linear in the number of connections and its accuracy surpasses the accuracy of existing ones in all considered scenarios.en
dc.identifier.citationTomic, S, Beko, M & Tuba, M 2019, 'A linear estimator for network localization using integrated RSS and AOA measurements', IEEE Signal Processing Letters, vol. 26, no. 3, 8607068, pp. 405-409. https://doi.org/10.1109/LSP.2019.2892225
dc.identifier.doihttps://doi.org/10.1109/LSP.2019.2892225
dc.identifier.issn1070-9908
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85061042581&partnerID=8YFLogxK
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Signal Processing Letters
dc.rightsclosedAccess
dc.subjectENGENHARIA ELETROTÉCNICA
dc.subjectREGRESSÕES LINEARES
dc.subjectRECEIVED SIGNAL STRENGTH
dc.subjectANGLE OF ARRIVAL
dc.subjectELECTROTECHNICAL ENGINEERING
dc.subjectLINEAR REGRESSIONS
dc.subjectRECEIVED SIGNAL STRENGTH
dc.subjectANGLE OF ARRIVAL
dc.titleA linear estimator for network localization using integrated RSS and AOA measurementsen
dc.typearticle

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