PDRNet: A Deep-Learning Pedestrian Dead Reckoning Framework

Omri Asraf, Firas Shama, Itzik Klein

Research output: Contribution to journalArticlepeer-review


Pedestrian dead reckoning is a well-known approach for indoor navigation. There, the smartphone's inertial sensors readings are used to determine the user position by utilizing empirical or bio-mechanical approaches and by direct integration. In this paper, we propose PDRNet, a deep-learning pedestrian dead reckoning framework, for user positioning. It includes a smartphone location recognition classification network followed by a change of heading and distance regression network. Experimental results using a publicly available dataset show that the proposed approach outperforms traditional approaches and other deep learning based ones.

Original languageEnglish
Pages (from-to)4932-4939
Number of pages8
JournalIEEE Sensors Journal
Issue number6
StatePublished - 15 Mar 2022

Bibliographical note

Publisher Copyright:
© 2001-2012 IEEE.


  • Pedestrian dead reckoning
  • deep-learning
  • indoor navigation
  • inertial sensors
  • residual networks
  • smartphone location recognition

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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