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Magnetic-Field-Based Indoor Positioning Using Temporal Convolutional Networks.

Sensors (Basel, Switzerland)·2023
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Analysis of Magnetic Field Measurements for Indoor Positioning.

Guanglie Ouyang1, Karim Abed-Meraim1

  • 1Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique, Université d'Orléans, 12 Rue de Blois, 45067 Orleans, France.

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Indoor positioning using magnetic fields (MF) is promising but faces challenges. This study analyzes MF characteristics, proposes anomaly elimination, and evaluates positioning performance, highlighting feasibility and limitations.

Keywords:
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Area of Science:

  • Geophysics and Signal Processing
  • Indoor Positioning Systems
  • Machine Learning Applications

Background:

  • Ubiquitous infrastructure-free magnetic fields (MF) offer potential for indoor positioning.
  • Challenges include low discernibility, device heterogeneity, and ferromagnetic interference.
  • Understanding MF statistical properties is crucial for reliable indoor localization.

Purpose of the Study:

  • Analyze statistical characteristics of MF measurements from heterogeneous smartphones.
  • Propose methods to eliminate MF anomalies and ensure measurement consistency.
  • Evaluate indoor positioning performance using MF data with various machine learning techniques.

Main Methods:

  • Statistical analysis of magnetic field (MF) measurements from heterogeneous smartphones.
  • Application of the RLOWESS method for anomaly elimination.
  • Magnetometer calibration for consistent MF data acquisition.
  • Testing positioning performance with diverse machine learning algorithms.

Main Results:

  • MF measurements in indoor environments exhibit Gaussian distribution, temporal stability, and spatial discernibility without disturbances.
  • Smartphone rotation around the Z-axis causes fluctuations in magnetic field intensity.
  • The RLOWESS method effectively eliminates magnetic field anomalies.
  • Consistent MF measurements across heterogeneous devices are achievable through magnetometer calibration.
  • Machine learning methods demonstrate varying performance for MF-based indoor positioning.

Conclusions:

  • Magnetic field-based indoor positioning is feasible but faces significant challenges.
  • Statistical analysis and anomaly elimination are key to improving accuracy.
  • Device heterogeneity and environmental interference remain critical limitations.
  • Further research is needed to overcome current limitations for practical applications.