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GNSS NLOS Signal Classification Based on Machine Learning and Pseudorange Residual Check
Global navigation satellite system (GNSS) positioning has recently garnered attention for autonomous driving, machine control, and construction sites. With the development of low-cost multi-GNSS receivers and the advent of new types of GNSS, such as Japan’s Quasi-Zenith Satellite System, the potenti...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9117752/ https://www.ncbi.nlm.nih.gov/pubmed/35603080 http://dx.doi.org/10.3389/frobt.2022.868608 |
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author | Ozeki, Tomohiro Kubo, Nobuaki |
author_facet | Ozeki, Tomohiro Kubo, Nobuaki |
author_sort | Ozeki, Tomohiro |
collection | PubMed |
description | Global navigation satellite system (GNSS) positioning has recently garnered attention for autonomous driving, machine control, and construction sites. With the development of low-cost multi-GNSS receivers and the advent of new types of GNSS, such as Japan’s Quasi-Zenith Satellite System, the potential of GNSS positioning has increased. New types of GNSS directly increase the number of line-of-sight (LOS) signals in dense urban areas and improve positioning accuracy. However, GNSS receivers can observe both LOS and non-line-of-sight (NLOS) signals in dense urban areas, and more NLOS signals are observed under static conditions than under dynamic conditions. The classification of LOS and NLOS signals is important, and various methods have been proposed, such as C/N0, using three-dimensional maps, fish-eye view, and GNSS/inertial navigation system integration. Multipath detection based on machine learning has also been reported in recent years. In this study, we propose a method for detecting NLOS signals using a support vector machine (SVM) classifier modeled with unique features that are calculated by receiver independent exchange format-based information and GNSS pseudorange residual check. We found that using both the SVM classifier and GNSS pseudorange residual check effectively reduced the error due to NLOS signals. Several static tests were conducted near high-rise buildings that are likely to receive some NLOS signals in downtown Tokyo. For all static tests, the percentage of positioning errors within 10 m in the horizontal positioning error was improved by >80% by detecting and eliminating satellites receiving NLOS signals. |
format | Online Article Text |
id | pubmed-9117752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91177522022-05-20 GNSS NLOS Signal Classification Based on Machine Learning and Pseudorange Residual Check Ozeki, Tomohiro Kubo, Nobuaki Front Robot AI Robotics and AI Global navigation satellite system (GNSS) positioning has recently garnered attention for autonomous driving, machine control, and construction sites. With the development of low-cost multi-GNSS receivers and the advent of new types of GNSS, such as Japan’s Quasi-Zenith Satellite System, the potential of GNSS positioning has increased. New types of GNSS directly increase the number of line-of-sight (LOS) signals in dense urban areas and improve positioning accuracy. However, GNSS receivers can observe both LOS and non-line-of-sight (NLOS) signals in dense urban areas, and more NLOS signals are observed under static conditions than under dynamic conditions. The classification of LOS and NLOS signals is important, and various methods have been proposed, such as C/N0, using three-dimensional maps, fish-eye view, and GNSS/inertial navigation system integration. Multipath detection based on machine learning has also been reported in recent years. In this study, we propose a method for detecting NLOS signals using a support vector machine (SVM) classifier modeled with unique features that are calculated by receiver independent exchange format-based information and GNSS pseudorange residual check. We found that using both the SVM classifier and GNSS pseudorange residual check effectively reduced the error due to NLOS signals. Several static tests were conducted near high-rise buildings that are likely to receive some NLOS signals in downtown Tokyo. For all static tests, the percentage of positioning errors within 10 m in the horizontal positioning error was improved by >80% by detecting and eliminating satellites receiving NLOS signals. Frontiers Media S.A. 2022-05-05 /pmc/articles/PMC9117752/ /pubmed/35603080 http://dx.doi.org/10.3389/frobt.2022.868608 Text en Copyright © 2022 Ozeki and Kubo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Ozeki, Tomohiro Kubo, Nobuaki GNSS NLOS Signal Classification Based on Machine Learning and Pseudorange Residual Check |
title | GNSS NLOS Signal Classification Based on Machine Learning and Pseudorange Residual Check |
title_full | GNSS NLOS Signal Classification Based on Machine Learning and Pseudorange Residual Check |
title_fullStr | GNSS NLOS Signal Classification Based on Machine Learning and Pseudorange Residual Check |
title_full_unstemmed | GNSS NLOS Signal Classification Based on Machine Learning and Pseudorange Residual Check |
title_short | GNSS NLOS Signal Classification Based on Machine Learning and Pseudorange Residual Check |
title_sort | gnss nlos signal classification based on machine learning and pseudorange residual check |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9117752/ https://www.ncbi.nlm.nih.gov/pubmed/35603080 http://dx.doi.org/10.3389/frobt.2022.868608 |
work_keys_str_mv | AT ozekitomohiro gnssnlossignalclassificationbasedonmachinelearningandpseudorangeresidualcheck AT kubonobuaki gnssnlossignalclassificationbasedonmachinelearningandpseudorangeresidualcheck |