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An Improved Long-Period Precise Time-Relative Positioning Method Based on RTS Data

The high precision positioning can be easily achieved by using real-time kinematic (RTK) and precise point positioning (PPP) or their augmented techniques, such as network RTK (NRTK) and PPP-RTK, even if they also have their own shortfalls. A reference station and datalink are required for RTK or NR...

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Autores principales: Lu, Yangwei, Ji, Shengyue, Tu, Rui, Weng, Duojie, Lu, Xiaochun, Chen, Wu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794687/
https://www.ncbi.nlm.nih.gov/pubmed/33374254
http://dx.doi.org/10.3390/s21010053
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author Lu, Yangwei
Ji, Shengyue
Tu, Rui
Weng, Duojie
Lu, Xiaochun
Chen, Wu
author_facet Lu, Yangwei
Ji, Shengyue
Tu, Rui
Weng, Duojie
Lu, Xiaochun
Chen, Wu
author_sort Lu, Yangwei
collection PubMed
description The high precision positioning can be easily achieved by using real-time kinematic (RTK) and precise point positioning (PPP) or their augmented techniques, such as network RTK (NRTK) and PPP-RTK, even if they also have their own shortfalls. A reference station and datalink are required for RTK or NRTK. Though the PPP technique can provide high accuracy position data, it needs an initialisation time of 10–30 min. The time-relative positioning method estimates the difference between positions at two epochs by means of a single receiver, which can overcome these issues within short period to some degree. The positioning error significantly increases for long-period precise positioning as consequence of the variation of various errors in GNSS (Global Navigation Satellite System) measurements over time. Furthermore, the accuracy of traditional time-relative positioning is very sensitive to the initial positioning error. In order to overcome these issues, an improved time-relative positioning algorithm is proposed in this paper. The improved time-relative positioning method employs PPP model to estimate the parameters of current epoch including position vector, float ionosphere-free (IF) ambiguities, so that these estimated float IF ambiguities are used as a constraint of the base epoch. Thus, the position of the base epoch can be estimated by means of a robust Kalman filter, so that the position of the current epoch with reference to the base epoch can be obtained by differencing the position vectors between the base epoch and the current one. The numerical results obtained during static and dynamic tests show that the proposed positioning algorithm can achieve a positioning accuracy of a few centimetres in one hour. As expected, the positioning accuracy is highly improved by combining GPS, BeiDou and Galileo as a consequence of a higher amount of used satellites and a more uniform geometrical distribution of the satellites themselves. Furthermore, the positioning accuracy achieved by using the positioning algorithm here described is not affected by the initial positioning error, because there is no approximation similar to that of the traditional time-relative positioning. The improved time-relative positioning method can be used to provide long-period high precision positioning by using a single dual-frequency (L1/L2) satellite receiver.
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spelling pubmed-77946872021-01-10 An Improved Long-Period Precise Time-Relative Positioning Method Based on RTS Data Lu, Yangwei Ji, Shengyue Tu, Rui Weng, Duojie Lu, Xiaochun Chen, Wu Sensors (Basel) Article The high precision positioning can be easily achieved by using real-time kinematic (RTK) and precise point positioning (PPP) or their augmented techniques, such as network RTK (NRTK) and PPP-RTK, even if they also have their own shortfalls. A reference station and datalink are required for RTK or NRTK. Though the PPP technique can provide high accuracy position data, it needs an initialisation time of 10–30 min. The time-relative positioning method estimates the difference between positions at two epochs by means of a single receiver, which can overcome these issues within short period to some degree. The positioning error significantly increases for long-period precise positioning as consequence of the variation of various errors in GNSS (Global Navigation Satellite System) measurements over time. Furthermore, the accuracy of traditional time-relative positioning is very sensitive to the initial positioning error. In order to overcome these issues, an improved time-relative positioning algorithm is proposed in this paper. The improved time-relative positioning method employs PPP model to estimate the parameters of current epoch including position vector, float ionosphere-free (IF) ambiguities, so that these estimated float IF ambiguities are used as a constraint of the base epoch. Thus, the position of the base epoch can be estimated by means of a robust Kalman filter, so that the position of the current epoch with reference to the base epoch can be obtained by differencing the position vectors between the base epoch and the current one. The numerical results obtained during static and dynamic tests show that the proposed positioning algorithm can achieve a positioning accuracy of a few centimetres in one hour. As expected, the positioning accuracy is highly improved by combining GPS, BeiDou and Galileo as a consequence of a higher amount of used satellites and a more uniform geometrical distribution of the satellites themselves. Furthermore, the positioning accuracy achieved by using the positioning algorithm here described is not affected by the initial positioning error, because there is no approximation similar to that of the traditional time-relative positioning. The improved time-relative positioning method can be used to provide long-period high precision positioning by using a single dual-frequency (L1/L2) satellite receiver. MDPI 2020-12-24 /pmc/articles/PMC7794687/ /pubmed/33374254 http://dx.doi.org/10.3390/s21010053 Text en © 2020 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
Lu, Yangwei
Ji, Shengyue
Tu, Rui
Weng, Duojie
Lu, Xiaochun
Chen, Wu
An Improved Long-Period Precise Time-Relative Positioning Method Based on RTS Data
title An Improved Long-Period Precise Time-Relative Positioning Method Based on RTS Data
title_full An Improved Long-Period Precise Time-Relative Positioning Method Based on RTS Data
title_fullStr An Improved Long-Period Precise Time-Relative Positioning Method Based on RTS Data
title_full_unstemmed An Improved Long-Period Precise Time-Relative Positioning Method Based on RTS Data
title_short An Improved Long-Period Precise Time-Relative Positioning Method Based on RTS Data
title_sort improved long-period precise time-relative positioning method based on rts data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794687/
https://www.ncbi.nlm.nih.gov/pubmed/33374254
http://dx.doi.org/10.3390/s21010053
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