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An Adaptive Method for Switching between Pedestrian/Car Indoor Positioning Algorithms based on Multilayer Time

Zhining Gu1,2, Wei Guo3,4, Chaoyang Li5

  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China. zhininggu@whu.edu.cn.

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|March 3, 2018
PubMed
Summary

This study introduces an adaptive method using multilayer time sequences (MTSs) to switch between pedestrian dead reckoning (PDR) and car indoor positioning algorithms. This improves location accuracy for various movement states like walking and driving.

Keywords:
MTSbehavior contextstate recognitionswitching pedestrian/car positioning algorithm

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Area of Science:

  • Indoor positioning systems
  • Sensor fusion
  • Behavior recognition

Background:

  • Pedestrian dead reckoning (PDR) is suitable for walking but not driving.
  • Bluetooth indoor positioning is effective for vehicular movement.
  • Accurate localization requires adapting to different movement states.

Purpose of the Study:

  • To propose an adaptive method for switching between PDR and car indoor positioning algorithms.
  • To enhance localization accuracy for diverse states (pedestrian, car, stationary).
  • To reduce the time delay associated with algorithm switching.

Main Methods:

  • Development of a multilayer time sequence (MTS) based adaptive switching method.
  • MTS incorporates data filtering for small time sequences and state chains for large time sequences.
  • Utilizes behavior context for accurate state recognition (stationary, walking, driving).

Main Results:

  • Significant improvements in state recognition: static (5.5%), walking (45.47%), driving (26.23%), other (21%) compared to CNN.
  • Reduced time delay for state transitions (0.5-8.5 s) and overall process (approx. 24 s).
  • Enhanced localization accuracy by adaptively switching between PDR and Bluetooth positioning.

Conclusions:

  • The proposed MTS-based adaptive method effectively recognizes various movement states.
  • The method successfully switches between appropriate indoor positioning algorithms.
  • This approach significantly improves localization accuracy and reduces switching time delays.