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Local attraction refers to disturbances in compass readings caused by magnetic influences from nearby objects such as metal fences, buried pipes, vehicles, buildings, power lines, or natural iron ore deposits. Small items like wristwatches, steel tools, or belt buckles can also interfere with the compass by creating local magnetic fields that distort the Earth's natural magnetic field. These distortions lead to inaccurate readings, posing navigation and land surveying challenges.Local...
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Magnetic-Map-Matching-Aided Pedestrian Navigation Using Outlier Mitigation Based on Multiple Sensors and Roughness

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This study introduces an improved indoor pedestrian navigation algorithm using magnetic field map matching to enhance position accuracy. The method effectively compensates for errors in pedestrian dead reckoning (PDR) for better indoor localization.

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Area of Science:

  • Robotics
  • Geomatics Engineering
  • Navigation Systems

Background:

  • Pedestrian dead reckoning (PDR) using zero velocity updates (ZUPT) suffers from uncompensated position errors, leading to divergence.
  • Accurate indoor localization is challenging due to the lack of easily obtainable position measurements, unlike in outdoor navigation.
  • Existing indoor navigation methods require significant improvements in position accuracy.

Purpose of the Study:

  • To propose a novel algorithm for accurate indoor pedestrian navigation.
  • To enhance position accuracy in pedestrian dead reckoning by mitigating error divergence.
  • To develop a magnetic field map-matching technique for reliable indoor localization.

Main Methods:

  • Implemented a magnetic field map-matching technique using multiple magnetic sensors and importance sampling for position determination.
  • Employed a normalization and roughness weighting method for outlier mitigation, improving sensor data reliability.
  • Utilized a 15th-order error model and an importance-sampling extended Kalman filter for precise error correction in map-matching-aided pedestrian dead reckoning (MAPDR).

Main Results:

  • The proposed magnetic field MAPDR algorithm demonstrated significant performance improvements in position accuracy compared to conventional PDR.
  • Experimental results confirmed the effectiveness of the algorithm across various indoor environments.
  • The integration of multiple sensors with advanced outlier mitigation enhanced the robustness of the navigation system.

Conclusions:

  • The developed magnetic field MAPDR algorithm offers a substantial advancement for accurate indoor pedestrian navigation.
  • The proposed method effectively compensates for position errors inherent in traditional PDR techniques.
  • This research provides a viable solution for precise indoor localization using magnetic field mapping and advanced filtering.