Data Fusion Approaches For WiFi Fingerprinting

Elena-Simona Lohan, Jukka Talvitie, Gonzalo Seco Granados

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    3 Citations (Scopus)
    42 Downloads (Pure)

    Abstract

    WiFi localization problem is basically a multi-sensor data fusion. This paper investigates the use of Bayesian and non-Bayesian Dempster Shafer (DS) data fusion in the context of WiFi-based indoor positioning via fingerprinting. Two novel DS mass choices are discussed. The positioning results are based on real-field measurement data from nine distinct multi-floor buildings in two countries. It is shown that a proper mass choice is crucial in DS processing and that, in spite of taking into account the data uncertainty, the DS data fusion is not offering significant advantage in terms of positioning performance over the Bayesian data fusion.
    Original languageEnglish
    Title of host publicationInternational Conference on Localization and GNSS (ICL-GNSS 2016)
    PublisherIEEE
    ISBN (Electronic)978-1-5090-1757-7
    DOIs
    Publication statusPublished - 2016
    Publication typeA4 Article in conference proceedings
    EventInternational Conference on Localization and GNSS -
    Duration: 1 Jan 1900 → …

    Publication series

    Name
    ISSN (Electronic)2325-0771

    Conference

    ConferenceInternational Conference on Localization and GNSS
    Period1/01/00 → …

    Publication forum classification

    • Publication forum level 1

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