MagWi: Practical Indoor Localization with Smartphone Magnetic and WiFi Sensors

Geomagnetic field and WiFi networks have attracted much attention in indoor localization, since they are pervasive, and their signals are easy to collect by off-the-shelf smartphones. However, there are still challenges in practice: 1) GFI (Geomagnetic Field intensity) read by magnetometer in smartp...

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Published in2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS) pp. 814 - 821
Main Authors Yuan, Hao, Wang, Jiankun, Zhao, Zenghua, Cui, Jiayang, Yan, Minglu, Wei, Shengen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
geomagnetic field
Indoor localization
mobile sensing
server fusion
WiFi RSS
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Summary:Geomagnetic field and WiFi networks have attracted much attention in indoor localization, since they are pervasive, and their signals are easy to collect by off-the-shelf smartphones. However, there are still challenges in practice: 1) GFI (Geomagnetic Field intensity) read by magnetometer in smartphones is hard to be used directly, since it is in phone coordinate system and varies with phone attitude. On the other hand, the magnitude of GFI lacks location diversity; 2) WiFi RSS (Received Signal Strength) suffers from fluctuations due to multipath fading effect indoors. To address the above issues, we first propose Mag2D, a high quality feature of GFI. Mag2D is robust to phone attitude and has better spatial discrimination than the magnitude. We then study the complementary properties of Mag2D and RSS, and design MagWi, a practical fingerprinting indoor localization system fusing Mag2D and RSS. MagWi adjusts fusion weights of Mag2D and RSS according to their capabilities of discriminating locations. To do so, we propose LCD (Local Clustering Degree) to quantify the location-discriminating capability and build a LCD-Error model. The fusion weights are thus assigned dynamically based on the LCD-Error model during localization. We have implemented MagWi to provide location service on an Android phone in three typical indoor environments, covering a total area size over 600m2. Our experiment results show that MagWi achieves high accuracy and is easy to deploy in practice.
DOI:10.1109/ICPADS47876.2019.00120