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GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation.

Pekka Peltola1, Jialin Xiao2, Terry Moore3

  • 1Centre for Automation and Robotics (CAR), Spanish Council for Scientific Research (CSIC-UPM), Ctra. de Campo Real km 0,200, Arganda del Rey, 28500 Madrid, Spain. pekka.peltola@csic.es.

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Summary
This summary is machine-generated.

This study detects Global Navigation Satellite System (GNSS) trajectory anomalies in urban areas by comparing GNSS data with trusted pedestrian dead reckoning. The Hausdorff distance and accumulated distance difference methods proved most effective for anomaly detection.

Keywords:
GNSS trajectoryanomaly detectionmultipathpedestrian dead reckoningsimilarity

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Area of Science:

  • Geomatics Engineering
  • Navigation Systems
  • Signal Processing

Background:

  • Urban environments pose significant challenges for Global Navigation Satellite System (GNSS) receivers.
  • Multipath propagation and signal anomalies in cities increase positioning errors and reduce the reliability of error estimates.
  • Accurate positioning is crucial for various applications, including autonomous navigation and location-based services.

Purpose of the Study:

  • To develop and evaluate methods for detecting anomalies in GNSS trajectories within urban settings.
  • To assess the effectiveness of comparing GNSS data with pedestrian dead reckoning (PDR) trajectories for anomaly identification.
  • To identify the most consistent similarity comparison methods for GNSS trajectory anomaly detection.

Main Methods:

  • Utilized a foot-mounted inertial measurement unit (IMU) to record a high-fidelity pedestrian dead reckoning (PDR) trajectory.
  • Collected a corresponding GNSS trajectory in an urban environment.
  • Applied eight different similarity comparison methods to evaluate the congruence between PDR and GNSS trajectories.
  • Focused on form similarity comparison techniques to identify deviations.

Main Results:

  • Pedestrian dead reckoning (PDR) provides a reliable reference trajectory for normal walking, accurate to within tens of meters.
  • Similarity comparison methods effectively detect deviations between GNSS and PDR trajectories, indicating anomalies.
  • The Hausdorff distance (HD) and Accumulated Distance Difference (ADD) emerged as the most consistent methods for detecting GNSS trajectory anomalies among the eight tested.

Conclusions:

  • Comparing PDR trajectories with GNSS data is a viable strategy for detecting anomalies in urban GNSS positioning.
  • The Hausdorff distance (HD) and Accumulated Distance Difference (ADD) are recommended for their robustness in identifying GNSS trajectory anomalies.
  • This approach enhances the reliability of positioning data in challenging urban environments.