Abstract
The smartphone magnetometer has been used in many indoor positioning systems to provide location information, such as orientation, user trajectory construction, and magnetic field-based fingerprint. However, suffering from magnetic disturbance, the magnetometer measurements are vulnerable to interference from metal infrastructures, electrical equipment, and other electronic devices in complex indoor environments. This paper extracts and explores the statistical features of the smartphone magnetometer measurements. Extensive experiments in various conditions show that the covariance and the magnitude difference can help detect the magnetic disturbance. Based on this, two unsupervised learning-based methods using Gaussian Mixture Model and k-means are developed to explore the two features mentioned above in magnetic disturbance detection. Experimental results demonstrate that the two proposed approaches have superior detection accuracy, which is 5% to 20% higher than the widely adopted vector selection methods in the literature.
Original language | English |
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Article number | 2506411 |
Pages (from-to) | 1-1 |
Number of pages | 1 |
Journal | IEEE Transactions on Instrumentation and Measurement |
Volume | 71 |
Early online date | 29 Mar 2022 |
DOIs | |
Publication status | E-pub ahead of print - 29 Mar 2022 |
Keywords / Materials (for Non-textual outputs)
- Magnetic field measurement
- Magnetometers
- Magnetic fields
- Feature extraction
- Magnetic devices
- Trajectory
- Fingerprint recognition
- smartphone
- magnetometer
- Clustering
- unsupervised learning
- indoor positioning system
- magnetic disturbance