An indoor collaborative pedestrian dead reckoning system

Yi Ting Li, Guaning Chen, Min Te Sun

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

10 Scopus citations


Indoor localization has become a popular topic in recent years. While self-contained pedestrian dead reckoning (PDR) systems can be conveniently implemented on a smartphone with built-in inertial sensors for indoor localization, the error of the estimated position for a PDR system can accumulate quickly and results in an unacceptable position accuracy. To address this issue, we propose the collaborative pedestrian dead reckoning (CPDR) system. The main idea of the CPDR system is when users are near to each other, we can leverage the proximity information to improve their estimated positions by means of the opportunistic Kalman filter. In addition, the backward correction scheme is used to improve the accuracy of user's trajectory. To evaluate the CPDR system, a prototype is implemented on Apple's iPhone 5. The experiment results show that the CPDR system achieves a better position accuracy than the raw PDR system.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationInternational Conference on Parallel Processing - The 42nd Annual Conference, ICPP 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Print)9780769551173
StatePublished - 2013
Event42nd Annual International Conference on Parallel Processing, ICPP 2013 - Lyon, France
Duration: 1 Oct 20134 Oct 2013

Publication series

NameProceedings of the International Conference on Parallel Processing
ISSN (Print)0190-3918


Conference42nd Annual International Conference on Parallel Processing, ICPP 2013


  • Dead reckoning
  • Indoor localization
  • Kalman filter


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