Indoor pedestrian tracking by on-body multiple receivers

Alejandro Correa, Marc Barcelo Llado, Antoni Morell, José López Vicario

Research output: Contribution to journalArticleResearchpeer-review

16 Citations (Scopus)

Abstract

© 2016 IEEE. During the past years, the development of indoor localization systems has been a hot topic in research, because the global navigation satellite systems suffer from a significant performance degradation due to the fact that the line of sight to the satellites is not available. The proposed system employs the received signal strength indicator from multiple anchor nodes from an operating wireless sensor network (WSN). In addition, we place multiple receivers around the user's body and thanks to machine learning techniques; we are able to estimate the distance and angle between the user and any of the anchor nodes of the WSN. This allows us to estimate the heading of the user without the use of inertial sensors or magnetometers. Finally, the user's position estimation is refined using an extended Kalman filter that considers the constant velocity kinematic model. The system has been validated in multiple real scenarios obtaining a root mean squared error around the meter for the different tests performed.
Original languageEnglish
Article number7384674
Pages (from-to)2545-2553
JournalIEEE Sensors Journal
Volume16
Issue number8
DOIs
Publication statusPublished - 15 Apr 2016

Keywords

  • Indoor localization
  • Kalman filters
  • Machine learning
  • Navigation
  • Wireless sensor networks

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