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GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation

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 comparis...

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Autores principales: Peltola, Pekka, Xiao, Jialin, Moore, Terry, Jiménez, Antonio R., Seco, Fernando
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165389/
https://www.ncbi.nlm.nih.gov/pubmed/30235863
http://dx.doi.org/10.3390/s18093165
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author Peltola, Pekka
Xiao, Jialin
Moore, Terry
Jiménez, Antonio R.
Seco, Fernando
author_facet Peltola, Pekka
Xiao, Jialin
Moore, Terry
Jiménez, Antonio R.
Seco, Fernando
author_sort Peltola, Pekka
collection PubMed
description 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.
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spelling pubmed-61653892018-10-10 GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation Peltola, Pekka Xiao, Jialin Moore, Terry Jiménez, Antonio R. Seco, Fernando Sensors (Basel) Article 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. MDPI 2018-09-19 /pmc/articles/PMC6165389/ /pubmed/30235863 http://dx.doi.org/10.3390/s18093165 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Peltola, Pekka
Xiao, Jialin
Moore, Terry
Jiménez, Antonio R.
Seco, Fernando
GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation
title GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation
title_full GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation
title_fullStr GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation
title_full_unstemmed GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation
title_short GNSS Trajectory Anomaly Detection Using Similarity Comparison Methods for Pedestrian Navigation
title_sort gnss trajectory anomaly detection using similarity comparison methods for pedestrian navigation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165389/
https://www.ncbi.nlm.nih.gov/pubmed/30235863
http://dx.doi.org/10.3390/s18093165
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