A Novel Bayesian Filter for RSS-Based Device-Free Localization and Tracking

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorKaltiokallio, Ossi
dc.contributor.authorHostettler, Roland
dc.contributor.authorPatwari, Neal
dc.contributor.departmentDepartment of Communications and Networking
dc.contributor.departmentUppsala University
dc.contributor.departmentWashington University St. Louis
dc.date.accessioned2021-02-26T07:13:49Z
dc.date.available2021-02-26T07:13:49Z
dc.date.issued2021-03-01
dc.description.abstractReceived signal strength based device-free localization applications utilize a model that relates the measurements to position of the wireless sensors and person, and the underlying inverse problem is solved either using an imaging method or a nonlinear Bayesian filter. In this paper, it is shown that the Bayesian filters nearly reach the posterior Cramér-Rao bound and they are superior with respect to imaging approaches in terms of localization accuracy because the measurements are directly related to position of the person. However, Bayesian filters are known to suffer from divergence issues and in this paper, the problem is addressed by introducing a novel Bayesian filter. The developed filter augments the measurement model of a Bayesian filter with position estimates from an imaging approach. This bounds the filter's measurement residuals by the position errors of the imaging approach and as an outcome, the developed filter has robustness of an imaging method and tracking accuracy of a Bayesian filter. The filter is demonstrated to achieve a localization error of 0.11 \text{ m}0.11m in a 75 \; \text{m}^275m2 open indoor deployment and an error of 0.29 \text{ m}0.29m in a 82 \; \text{m}^282m2 apartment experiment, decreasing the localization error by 30-48 percent with respect to a state-of-the-art imaging method.en
dc.description.versionPeer revieweden
dc.format.extent16
dc.format.extent780-795
dc.format.mimetypeapplication/pdf
dc.identifier.citationKaltiokallio , O , Hostettler , R & Patwari , N 2021 , ' A Novel Bayesian Filter for RSS-Based Device-Free Localization and Tracking ' , IEEE Transactions on Mobile Computing , vol. 20 , no. 3 , 8931256 , pp. 780-795 . https://doi.org/10.1109/TMC.2019.2953474en
dc.identifier.doi10.1109/TMC.2019.2953474
dc.identifier.issn1536-1233
dc.identifier.issn1558-0660
dc.identifier.otherPURE UUID: 874626ed-fbc6-4175-b895-dbd5ecdb3adf
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/874626ed-fbc6-4175-b895-dbd5ecdb3adf
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85100745257&partnerID=8YFLogxK
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/56284030/Kaltokallio_Novel_bayesian_filter_for_rss_feed_device_free_localization.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/102796
dc.identifier.urnURN:NBN:fi:aalto-202102262085
dc.language.isoenen
dc.publisherIEEE
dc.relation.ispartofseriesIEEE Transactions on Mobile Computingen
dc.relation.ispartofseriesVolume 20, issue 3en
dc.rightsopenAccessen
dc.subject.keywordBayesian filtering
dc.subject.keywordpositioning and tracking
dc.subject.keywordposterior Cramér-Rao bound
dc.subject.keywordReceived signal strength
dc.subject.keywordwireless sensor networks
dc.titleA Novel Bayesian Filter for RSS-Based Device-Free Localization and Trackingen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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