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Using the MNL Model in a Mobile Device's Indoor Positioning.
Feng Xie1, Ming Xie2, Cheng Wang1
1School of Information Science and Technology, Sanda University, Shanghai 201209, China.
Biomimetics (Basel, Switzerland)
|June 27, 2023
Summary
This study introduces a new Wi-Fi fingerprinting method for accurate indoor positioning services (IPS). The approach uses the Multinomial Logit Model (MNL) to achieve real-time location determination with approximately 3-meter accuracy.
Area of Science:
- Computer Science
- Electrical Engineering
- Geomatics Engineering
Background:
- Indoor Positioning Services (IPS) are crucial for navigation and information access in complex environments.
- Wi-Fi-based indoor positioning leverages existing Wireless Local Area Networks (WLAN), offering significant market potential.
- Accurate real-time localization remains a challenge in large indoor venues.
Purpose of the Study:
- To present a novel method for real-time Wi-Fi signal fingerprinting for indoor positioning.
- To evaluate the accuracy and feasibility of the proposed positioning technique.
- To enhance the performance of mobile device localization in commercial and public spaces.
Main Methods:
- Implementation of the Multinomial Logit Model (MNL) for generating Wi-Fi signal fingerprints.
- Real-time data collection and processing of Wi-Fi signal strengths.
- Experimental validation involving 31 randomly selected test locations.
Main Results:
- The proposed MNL-based method demonstrated effective real-time indoor positioning.
- Mobile devices achieved an average location accuracy of approximately 3 meters.
- The median accuracy in experimental tests was recorded at 2.53 meters.
Conclusions:
- The Multinomial Logit Model provides a viable and accurate solution for Wi-Fi-based indoor positioning.
- The method shows promise for widespread application in various large commercial and transit hubs.
- Real-time localization accuracy around 3 meters is achievable with this approach.
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