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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.
Sensors (Basel, Switzerland)
|February 17, 2019
Summary
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.
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.
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