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Cloud-Based Single-Frequency Snapshot RTK Positioning

With great potential for being applied to Internet of Things (IoT) applications, the concept of cloud-based Snapshot Real Time Kinematics (SRTK) was proposed and its feasibility under zero-baseline configuration was confirmed recently by the authors. This article first introduces the general workflo...

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Autores principales: Liu, Xiao, Ribot, Miguel Ángel, Gusi-Amigó, Adrià, Rovira-Garcia, Adria, Sanz, Jaume, Closas, Pau
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8199402/
https://www.ncbi.nlm.nih.gov/pubmed/34073194
http://dx.doi.org/10.3390/s21113688
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author Liu, Xiao
Ribot, Miguel Ángel
Gusi-Amigó, Adrià
Rovira-Garcia, Adria
Sanz, Jaume
Closas, Pau
author_facet Liu, Xiao
Ribot, Miguel Ángel
Gusi-Amigó, Adrià
Rovira-Garcia, Adria
Sanz, Jaume
Closas, Pau
author_sort Liu, Xiao
collection PubMed
description With great potential for being applied to Internet of Things (IoT) applications, the concept of cloud-based Snapshot Real Time Kinematics (SRTK) was proposed and its feasibility under zero-baseline configuration was confirmed recently by the authors. This article first introduces the general workflow of the SRTK engine, as well as a discussion on the challenges of achieving an SRTK fix using actual snapshot data. This work also describes a novel solution to ensure a nanosecond level absolute timing accuracy in order to compute highly precise satellite coordinates, which is required for SRTK. Parameters such as signal bandwidth, integration time and baseline distances have an impact on the SRTK performance. To characterize this impact, different combinations of these settings are analyzed through experimental tests. The results show that the use of higher signal bandwidths and longer integration times result in higher SRTK fix rates, while the more significant impact on the performance comes from the baseline distance. The results also show that the SRTK fix rate can reach more than 93% by using snapshots with a data size as small as 255 kB. The positioning accuracy is at centimeter level when phase ambiguities are resolved at a baseline distance less or equal to 15 km.
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spelling pubmed-81994022021-06-14 Cloud-Based Single-Frequency Snapshot RTK Positioning Liu, Xiao Ribot, Miguel Ángel Gusi-Amigó, Adrià Rovira-Garcia, Adria Sanz, Jaume Closas, Pau Sensors (Basel) Article With great potential for being applied to Internet of Things (IoT) applications, the concept of cloud-based Snapshot Real Time Kinematics (SRTK) was proposed and its feasibility under zero-baseline configuration was confirmed recently by the authors. This article first introduces the general workflow of the SRTK engine, as well as a discussion on the challenges of achieving an SRTK fix using actual snapshot data. This work also describes a novel solution to ensure a nanosecond level absolute timing accuracy in order to compute highly precise satellite coordinates, which is required for SRTK. Parameters such as signal bandwidth, integration time and baseline distances have an impact on the SRTK performance. To characterize this impact, different combinations of these settings are analyzed through experimental tests. The results show that the use of higher signal bandwidths and longer integration times result in higher SRTK fix rates, while the more significant impact on the performance comes from the baseline distance. The results also show that the SRTK fix rate can reach more than 93% by using snapshots with a data size as small as 255 kB. The positioning accuracy is at centimeter level when phase ambiguities are resolved at a baseline distance less or equal to 15 km. MDPI 2021-05-26 /pmc/articles/PMC8199402/ /pubmed/34073194 http://dx.doi.org/10.3390/s21113688 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Xiao
Ribot, Miguel Ángel
Gusi-Amigó, Adrià
Rovira-Garcia, Adria
Sanz, Jaume
Closas, Pau
Cloud-Based Single-Frequency Snapshot RTK Positioning
title Cloud-Based Single-Frequency Snapshot RTK Positioning
title_full Cloud-Based Single-Frequency Snapshot RTK Positioning
title_fullStr Cloud-Based Single-Frequency Snapshot RTK Positioning
title_full_unstemmed Cloud-Based Single-Frequency Snapshot RTK Positioning
title_short Cloud-Based Single-Frequency Snapshot RTK Positioning
title_sort cloud-based single-frequency snapshot rtk positioning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8199402/
https://www.ncbi.nlm.nih.gov/pubmed/34073194
http://dx.doi.org/10.3390/s21113688
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