Recursive Bayesian Filters for RSS-Based Device-Free Localization and Tracking

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorKaltiokallio, Ossien_US
dc.contributor.authorHostettler, Rolanden_US
dc.contributor.authorPatwari, Nealen_US
dc.contributor.authorJäntti, Rikuen_US
dc.contributor.departmentDepartment of Communications and Networkingen_US
dc.contributor.departmentDepartment of Electrical Engineering and Automationen_US
dc.contributor.departmentUniversity of Utahen_US
dc.date.accessioned2019-01-30T15:07:43Z
dc.date.available2019-01-30T15:07:43Z
dc.date.issued2018-11-13en_US
dc.description.abstractReceived signal strength (RSS)-based device-free localization applications utilize the communication between wireless devices for locating people within the monitored area. The technology is based on the fact that humans cause changes in properties of the wireless channel which is observed in the RSS, enabling localization of people without requiring them to carry any sensor, tag or device. Typically this inverse problem is solved using an empirical model that relates the RSS to location of the sensors and person, and utilizing either an imaging method or a particle filter (PF) for positioning. In this paper, we present an extended Kalman filtering (EKF) solution that incorporates some of the beneficial properties of the PF but has a lower computational overhead. In order to make the EKF work, we also need to reconsider how the measurements are sampled and processed, and a new processing scheme is proposed. The developments are validated using simulations and experimental data, and the results imply: i) the non-linear filters outperform a popular imaging method; ii) the robustness of the EKF and PF is improved using the proposed processing scheme; and iii) the EKF achieves similar performance as the PF as long as the new processing scheme is used.en
dc.description.versionPeer revieweden
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationKaltiokallio , O , Hostettler , R , Patwari , N & Jäntti , R 2018 , Recursive Bayesian Filters for RSS-Based Device-Free Localization and Tracking . in IPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigation . , 8533772 , International Conference on Indoor Positioning and Indoor Navigation , IEEE , United States , International Conference on Indoor Positioning and Indoor Navigation , Nantes , France , 24/09/2018 . https://doi.org/10.1109/IPIN.2018.8533772en
dc.identifier.doi10.1109/IPIN.2018.8533772en_US
dc.identifier.isbn9781538656358
dc.identifier.issn2162-7347
dc.identifier.issn2471-917X
dc.identifier.otherPURE UUID: 2a71f894-c9b6-4e02-bdca-f759655332aaen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/2a71f894-c9b6-4e02-bdca-f759655332aaen_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85059078161&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/31241798/ELEC_Kaltokallio_et_al_Recursive_Bayesian_Filters_IPIN2018.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/36232
dc.identifier.urnURN:NBN:fi:aalto-201901301402
dc.language.isoenen
dc.relation.ispartofInternational Conference on Indoor Positioning and Indoor Navigationen
dc.relation.ispartofseriesIPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigationen
dc.relation.ispartofseriesInternational Conference on Indoor Positioning and Indoor Navigationen
dc.rightsopenAccessen
dc.subject.keywordBayesian filteringen_US
dc.subject.keywordpositioning and trackingen_US
dc.subject.keywordreceived signal strengthen_US
dc.subject.keywordwireless sensor networksen_US
dc.titleRecursive Bayesian Filters for RSS-Based Device-Free Localization and Trackingen
dc.typeConference article in proceedingsfi
dc.type.versionacceptedVersion
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