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Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization
Ahmed H Salamah1,2, Mohamed Tamazin3, Maha A Sharkas4
1Electrical and Computer Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada. AhmedHuss.Salamah@aast.edu.
Sensors (Basel, Switzerland)
|April 11, 2019
Summary
This study enhances indoor positioning using Principal Component Analysis (PCA) with Wi-Fi fingerprinting. The improved machine learning approach offers superior accuracy and efficiency for locating smartphones indoors.
Area of Science:
- Computer Science
- Electrical Engineering
- Geomatics Engineering
Background:
- Accurate indoor localization is crucial for numerous smartphone services.
- Global Positioning System (GPS) accuracy degrades significantly indoors.
- Wi-Fi Fingerprinting is a common method for indoor positioning.
Purpose of the Study:
- To improve the performance of Wi-Fi indoor localization systems.
- To reduce the computational complexity of Wi-Fi indoor localization.
- To leverage machine learning with Principal Component Analysis (PCA) for enhanced indoor positioning.
Main Methods:
- Implemented Principal Component Analysis (PCA) to optimize Wi-Fi fingerprinting data.
- Utilized a machine learning approach for indoor localization.
- Conducted experiments in a large-scale indoor environment (960 m²).
Main Results:
- The proposed PCA-enhanced method demonstrated superior performance compared to conventional techniques.
- Reduced computational complexity was observed.
- Accurate localization was achieved in real-world indoor settings.
Conclusions:
- Principal Component Analysis (PCA) effectively enhances Wi-Fi fingerprinting for indoor localization.
- The proposed machine learning approach offers a more accurate and efficient solution for indoor positioning needs.
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