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PPP Sliding Window Algorithm and Its Application in Deformation Monitoring
Compared with the double-difference relative positioning method, the precise point positioning (PPP) algorithm can avoid the selection of a static reference station and directly measure the three-dimensional position changes at the observation site and exhibit superiority in a variety of deformation...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4886509/ https://www.ncbi.nlm.nih.gov/pubmed/27241172 http://dx.doi.org/10.1038/srep26497 |
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author | Song, Weiwei Zhang, Rui Yao, Yibin Liu, Yanyan Hu, Yuming |
author_facet | Song, Weiwei Zhang, Rui Yao, Yibin Liu, Yanyan Hu, Yuming |
author_sort | Song, Weiwei |
collection | PubMed |
description | Compared with the double-difference relative positioning method, the precise point positioning (PPP) algorithm can avoid the selection of a static reference station and directly measure the three-dimensional position changes at the observation site and exhibit superiority in a variety of deformation monitoring applications. However, because of the influence of various observing errors, the accuracy of PPP is generally at the cm-dm level, which cannot meet the requirements needed for high precision deformation monitoring. For most of the monitoring applications, the observation stations maintain stationary, which can be provided as a priori constraint information. In this paper, a new PPP algorithm based on a sliding window was proposed to improve the positioning accuracy. Firstly, data from IGS tracking station was processed using both traditional and new PPP algorithm; the results showed that the new algorithm can effectively improve positioning accuracy, especially for the elevation direction. Then, an earthquake simulation platform was used to simulate an earthquake event; the results illustrated that the new algorithm can effectively detect the vibrations change of a reference station during an earthquake. At last, the observed Wenchuan earthquake experimental results showed that the new algorithm was feasible to monitor the real earthquakes and provide early-warning alerts. |
format | Online Article Text |
id | pubmed-4886509 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-48865092016-06-08 PPP Sliding Window Algorithm and Its Application in Deformation Monitoring Song, Weiwei Zhang, Rui Yao, Yibin Liu, Yanyan Hu, Yuming Sci Rep Article Compared with the double-difference relative positioning method, the precise point positioning (PPP) algorithm can avoid the selection of a static reference station and directly measure the three-dimensional position changes at the observation site and exhibit superiority in a variety of deformation monitoring applications. However, because of the influence of various observing errors, the accuracy of PPP is generally at the cm-dm level, which cannot meet the requirements needed for high precision deformation monitoring. For most of the monitoring applications, the observation stations maintain stationary, which can be provided as a priori constraint information. In this paper, a new PPP algorithm based on a sliding window was proposed to improve the positioning accuracy. Firstly, data from IGS tracking station was processed using both traditional and new PPP algorithm; the results showed that the new algorithm can effectively improve positioning accuracy, especially for the elevation direction. Then, an earthquake simulation platform was used to simulate an earthquake event; the results illustrated that the new algorithm can effectively detect the vibrations change of a reference station during an earthquake. At last, the observed Wenchuan earthquake experimental results showed that the new algorithm was feasible to monitor the real earthquakes and provide early-warning alerts. Nature Publishing Group 2016-05-31 /pmc/articles/PMC4886509/ /pubmed/27241172 http://dx.doi.org/10.1038/srep26497 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Song, Weiwei Zhang, Rui Yao, Yibin Liu, Yanyan Hu, Yuming PPP Sliding Window Algorithm and Its Application in Deformation Monitoring |
title | PPP Sliding Window Algorithm and Its Application in Deformation Monitoring |
title_full | PPP Sliding Window Algorithm and Its Application in Deformation Monitoring |
title_fullStr | PPP Sliding Window Algorithm and Its Application in Deformation Monitoring |
title_full_unstemmed | PPP Sliding Window Algorithm and Its Application in Deformation Monitoring |
title_short | PPP Sliding Window Algorithm and Its Application in Deformation Monitoring |
title_sort | ppp sliding window algorithm and its application in deformation monitoring |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4886509/ https://www.ncbi.nlm.nih.gov/pubmed/27241172 http://dx.doi.org/10.1038/srep26497 |
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