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Summary

This study introduces a deep learning method to improve indoor magnetic positioning accuracy by fusing temporal and spatial magnetic fingerprint features. The approach enhances stability and trustworthiness for reliable navigation in diverse indoor environments.

Keywords:
indoor positioningmagnetic fingerprintmulti-scale featurestrajectory extraction

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Area of Science:

  • Geomatics Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Indoor positioning systems (IPS) often rely on magnetic fingerprints, but limited data dynamic range affects accuracy.
  • Existing methods struggle with the inherent variability of magnetic field data, impacting positioning reliability.

Purpose of the Study:

  • To develop a deep learning-based method for fusing temporal and spatial magnetic fingerprint characteristics.
  • To enhance the accuracy and stability of indoor magnetic positioning by leveraging multi-scale features.
  • To address the limitations of weak magnetic fields in achieving trustworthy positioning results.

Main Methods:

  • Simulated pedestrian trajectories using an improved random waypoint model.
  • Generated magnetic sequences by mapping magnetic data.
  • Designed a scale transformation unit to extract multi-scale features from magnetic sequences.
  • Employed a neural network self-attention mechanism to fuse multi-scale features for positioning.

Main Results:

  • Achieved average positioning errors of 0.65 m in corridors, 0.93 m in open areas, and 1.38 m in complex areas.
  • Demonstrated adaptability to diverse indoor scenes.
  • Validated the effectiveness of multi-scale features in improving positioning performance.

Conclusions:

  • The proposed deep learning method effectively fuses temporal and spatial magnetic fingerprint features for robust indoor positioning.
  • The integration of multi-scale features significantly enhances positioning accuracy and stability across various indoor environments.
  • This approach offers a promising solution for reliable indoor navigation using magnetic field data.