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A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model.

Yi Lu1,2, Dongyan Wei3, Qifeng Lai4,5

  • 1Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing 100094, China. luyi_aoe@163.com.

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|December 6, 2016
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Summary
This summary is machine-generated.

This study introduces a context-aware model to improve indoor positioning using pedestrian dead reckoning (PDR). By recognizing environmental context, it significantly enhances PDR accuracy and stability for smartphone navigation.

Keywords:
HMMPDRcontext recognitionindoor localizationturn detection

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Area of Science:

  • Computer Science
  • Robotics
  • Geomatics Engineering

Background:

  • Global Navigation Satellite Systems (GNSS) are ineffective indoors, necessitating alternative indoor positioning solutions.
  • Pedestrian Dead Reckoning (PDR) using smartphone inertial sensors is a viable indoor localization technique but suffers from cumulative errors.
  • External calibration sources are required to mitigate PDR's inherent positioning inaccuracies.

Purpose of the Study:

  • To propose a novel context-recognition-aided Pedestrian Dead Reckoning (PDR) localization model for indoor environments.
  • To enhance the accuracy and stability of PDR by integrating context-based calibration.
  • To reduce computational complexity and storage requirements for PDR systems.

Main Methods:

  • A context-recognition-aided PDR localization model was developed, utilizing human actions and environmental objects for context detection.
  • Hidden Markov Model (HMM) and Recursive Viterbi Algorithm were employed for efficient context matching against a pre-stored database.
  • A turn detection algorithm was designed, with corner context used for model illustration and verification.

Main Results:

  • The proposed model effectively calibrates PDR, fixing the pedestrian's starting point rapidly.
  • Indoor positioning accuracy was improved by up to 40.56% compared to traditional PDR methods.
  • The localization method demonstrated excellent stability and robustness in experimental evaluations.

Conclusions:

  • Context-recognition-aided PDR offers a significant improvement over standard PDR for indoor navigation.
  • The integration of HMM and Recursive Viterbi Algorithm provides an efficient and robust solution for context matching.
  • This approach enhances the practical applicability of smartphone-based indoor positioning systems.