Kalman-Type Filters and Smoothers for Pedestrian Dead Reckoning

Pavel Ivanov, Matti Raitoharju, Robert Piche

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

1 Citation (Scopus)

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 publicationProceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2018
PublisherIEEE
Number of pages7
ISBN (Electronic)9781538656358
DOIs
Publication statusPublished - 13 Nov 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Indoor Positioning and Indoor Navigation - Nantes, France
Duration: 24 Sep 201827 Sep 2018
Conference number: 9

Publication series

NameInternational Conference on Indoor Positioning and Indoor Navigation
PublisherIEEE
ISSN (Print)2162-7347
ISSN (Electronic)2471-917X

Conference

ConferenceInternational Conference on Indoor Positioning and Indoor Navigation
Abbreviated titleIPIN
CountryFrance
CityNantes
Period24/09/201827/09/2018

Keywords

  • Kalman filters
  • length measurement
  • noise measurement
  • global navigation
  • satellite system
  • time measurement
  • covariance matrices
  • estimation

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