Kalman-Type Filters and Smoothers for Pedestrian Dead Reckoning

Pavel Ivanov, Matti Raitoharju, Robert Piché

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

    4 Citations (Scopus)
    224 Downloads (Pure)

    Abstract

    In this paper, we present a method for device localization based on the fusion of location data from Global Navigation Satellite System and data from inertial sensors. We use a Kalman filter as well as its non-linear variants for realtime position estimation, and corresponding smoothers for offline position estimation. In all filters we use information about changes of user's heading, which are computed from the acceleration and gyroscope data. Models used with Extended and Unscented Kalman filters also take into account information about step length, whereas Kalman Filter does not, because the measurement is non-linear. In order to overcome this shortcoming, we introduce a modified Kalman Filter which adjusts the state vector according to the step length measurements. Our experiments show that use of step length information does not significantly improve performance when location measurements are constantly available. However, in real situations, when location data is partially unavailable, information about step length and its appropriate integration into the filter design is important, and improve localization accuracy considerably.

    Original languageEnglish
    Title of host publicationIPIN 2018 - 9th International Conference on Indoor Positioning and Indoor Navigation
    PublisherIEEE
    Number of pages7
    ISBN (Electronic)9781538656358
    DOIs
    Publication statusPublished - 13 Nov 2018
    Publication typeA4 Article in conference proceedings
    EventInternational Conference on Indoor Positioning and Indoor Navigation - Nantes, France
    Duration: 24 Sept 201827 Sept 2018

    Publication series

    Name
    ISSN (Electronic)2471-917X

    Conference

    ConferenceInternational Conference on Indoor Positioning and Indoor Navigation
    Abbreviated titleIPIN 2018
    Country/TerritoryFrance
    CityNantes
    Period24/09/1827/09/18

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Control and Optimization

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