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Sensors 2018, 18(9), 3165; https://doi.org/10.3390/s18093165

GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation

1
Centre 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
2
Nottingham Geospatial Institute, The University of Nottingham, Triumph Road, Nottingham NG7 2TU, UK
*
Author to whom correspondence should be addressed.
Received: 15 June 2018 / Revised: 12 September 2018 / Accepted: 14 September 2018 / Published: 19 September 2018
(This article belongs to the Special Issue GNSS and Fusion with Other Sensors)
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Abstract

The urban setting is a challenging environment for GNSS receivers. Multipath and other anomalies typically increase the positioning error of the receiver. Moreover, the error estimate of the position is often unreliable. In this study, we detect GNSS trajectory anomalies by using similarity comparison methods between a pedestrian dead reckoning trajectory, recorded using a foot-mounted inertial measurement unit, and the corresponding GNSS trajectory. During a normal walk, the foot-mounted inertial dead reckoning setup is trustworthy up to a few tens of meters. Thus, the differing GNSS trajectory can be detected using form similarity comparison methods. Of the eight tested methods, the Hausdorff distance (HD) and the accumulated distance difference (ADD) give slightly more consistent detection results compared to the rest. View Full-Text
Keywords: similarity; GNSS trajectory; pedestrian dead reckoning; multipath; anomaly detection similarity; GNSS trajectory; pedestrian dead reckoning; multipath; anomaly detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Peltola, P.; Xiao, J.; Moore, T.; Jiménez, A.R.; Seco, F. GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation. Sensors 2018, 18, 3165.

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