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A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning.

Yida Zhu1, Haiyong Luo2, Qu Wang3

  • 1School of Software Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China. dozenpiggy@bupt.edu.cn.

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|February 17, 2019
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Summary
This summary is machine-generated.

This study introduces a new method for accurately detecting indoor/outdoor transitions using smartphone Global Navigation Satellite System (GNSS) data. The approach enhances seamless positioning and navigation services by quickly identifying location changes.

Keywords:
GNSS measurementsindoor/outdoor detectionmachine learningquickly switchingseamless indoor and outdoor navigation and positioningsmartphone

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Area of Science:

  • Geomatics Engineering
  • Mobile Computing
  • Signal Processing

Background:

  • Location-Based Services (LBS) are widely used on smartphones.
  • Seamless indoor/outdoor positioning requires accurate detection of transitions.
  • Detecting Indoor/Outdoor (IO) transitions is challenging due to signal variations and ambiguity.

Purpose of the Study:

  • To develop a novel method for rapid and accurate detection of Indoor/Outdoor (IO) transitions.
  • To address the challenges of IO detection in complex scenarios and transition regions.
  • To improve the performance of LBS by enhancing IO state recognition.

Main Methods:

  • Analysis and extraction of spatial, temporal, and statistical features from GNSS measurements on Android smartphones.
  • Development of an ensemble model using stacking for IO detection.
  • Filtering of detection results with a Hidden Markov Model (HMM).

Main Results:

  • Achieved 99.11% accuracy in known IO environments and 97.02% in new scenarios.
  • IO transition recognition accuracy reached 94.53% (known) and 92.80% (new).
  • Over 80% probability of switching delay within 3-4 seconds in transition scenarios.

Conclusions:

  • The proposed ensemble model effectively detects IO transitions with high accuracy.
  • The method significantly improves the speed and reliability of IO switching detection.
  • This advancement supports more robust and seamless LBS applications.