Bayesian methodology for target tracking using combined RSS and AoA measurements

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dc.contributor.authorTomic, Slavisa
dc.contributor.authorBeko, Marko
dc.contributor.authorDinis, Rui
dc.contributor.authorTuba, Milan
dc.contributor.authorBacanin, Nebojsa
dc.contributor.institutionECATI - School of Communication, Architecture, Arts and Information Technologies
dc.date.issued2017
dc.descriptionPhysical Communication 25 (2017) pág.158–166
dc.description.abstractThis work addresses the target tracking problem based on received signal strength (RSS) and angle of arrival (AoA) measurements. The Bayesian methodology, which integrates the information given by observations with prior knowledge extracted from target motion model in order to enhance the estimation accuracy was employed. First, by converting the considered highly non-linear measurement model into a linear one, i.e., a novel linearization technique of the measurement model is proposed. The derived model is then merged with the prior knowledge, and a novel maximum a posteriori (MAP) estimator whose solution is given in closed-form is proposed. It is also shown that the Kalman filter (KF) can be directly applied on top of the linearized observation model, which results in a proposal of a novel KF algorithm. Furthermore, to the best of authors’ knowledge, this paper premierly presents the application of the extended KF (EKF) and the unscented KF (UKF) to the considered tracking problem, by applying first-order linearization technique to the original non-linear model, and by applying the unscented transformation to carefully selected sample points, respectively. Finally, importance weights are computed for a large number of randomly selected sample points to render a well-known particle filter (PF) solution. Simulation results show that the proposed algorithms perform better than a naive one which uses only information from observations. They also confirm the effectiveness of the proposed linearization technique in comparison with the existing one, reducing the estimation error for about 25%.en
dc.formatapplication/pdf
dc.identifier.citationTomic, S, Beko, M, Dinis, R, Tuba, M & Bacanin, N 2017, 'Bayesian methodology for target tracking using combined RSS and AoA measurements', Physical Communication.
dc.identifier.issn1874-4907
dc.identifier.urlhttps://www.scopus.com/pages/publications/85032812681
dc.language.isoeng
dc.peerreviewedno
dc.publisherElsevier B.V.
dc.relation.ispartofPhysical Communication
dc.rightsopenAccess
dc.subjectRECEIVED SIGNAL STRENGTH
dc.subjectANGLE OF ARRIVAL
dc.subjectENGENHARIA ELETROTÉCNICA
dc.subjectFILTRO DE KALMAN
dc.subjectTARGET TRACKING
dc.subjectTARGET TRACKING
dc.subjectRECEIVED SIGNAL STRENGTH
dc.subjectANGLE OF ARRIVAL
dc.subjectKALMAN FILTER
dc.subjectELECTROTECHNICAL ENGINEERING
dc.titleBayesian methodology for target tracking using combined RSS and AoA measurementsen
dc.typearticle

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