A Comparison of Bayesian Localization Methods in the Presence of Outliers

Nunzia Ferrara, Henk Wymeersch, Elena-Simona Lohan, Jari Nurmi

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

    Abstract

    Localization of a user in a wireless network is challenging in the presence of malfunctioning or malicious reference nodes, since if they are not accounted for, large localization errors can ensue. We evaluate three Bayesian methods to statistically identify outliers during localization: an exact method, an expectation maximization (EM) method proposed earlier, and a new method based on Variational Bayesian EM (VBEM). Simulation results indicate similar performance for the latter two schemes, with the VBEM algorithm able to provide a statistical description of the user location, rather than an estimate as in the simpler EM case. In contrast to previous studies, we find that there is a significant gap between the approximate methods and the exact method, the cause of which is discussed.
    Original languageEnglish
    Title of host publication2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017
    PublisherIEEE
    Pages1546-1551
    Number of pages6
    ISBN (Electronic)9781509043729
    DOIs
    Publication statusPublished - Jun 2017
    Publication typeA4 Article in conference proceedings
    EventInternational Wireless Communications and Mobile Computing Conference -
    Duration: 1 Jan 2000 → …

    Publication series

    Name
    ISSN (Electronic)2376-6506

    Conference

    ConferenceInternational Wireless Communications and Mobile Computing Conference
    Period1/01/00 → …

    Publication forum classification

    • Publication forum level 1

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