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Online stochastic modelling for network-based GPS real-time kinematic positioning

Baseline length-dependent errors in GPS RTK positioning, such as orbit uncertainty, and atmospheric effects, constrain the applicable baseline length between reference and mobile user receiver to perhaps 10-15km. This constraint has led to the development of network-based RTK techniques to model suc...

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Main Authors: Wang, Jinling, Lee, Hung Kyu, Lee, Young-Jin, Musa, Tajul A., Rizos, Chris
Format: Conference or Workshop Item
Language:English
Published: 2004
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/1169/1/WANG%2C_Jinling_P186.pdf
http://eprints.utm.my/id/eprint/1169/
http://www.gnss2004.org
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spelling my.utm.11692017-08-30T07:36:16Z http://eprints.utm.my/id/eprint/1169/ Online stochastic modelling for network-based GPS real-time kinematic positioning Wang, Jinling Lee, Hung Kyu Lee, Young-Jin Musa, Tajul A. Rizos, Chris TA Engineering (General). Civil engineering (General) Baseline length-dependent errors in GPS RTK positioning, such as orbit uncertainty, and atmospheric effects, constrain the applicable baseline length between reference and mobile user receiver to perhaps 10-15km. This constraint has led to the development of network-based RTK techniques to model such distance-dependent errors. Although these errors can be effectively mitigated by network-based techniques, the residual errors, attributed to imperfect network functional models, in practice, affect the positioning performance. Since it is too difficult for the functional model to define and/or handle the residual errors, an alternative approach that can be used is to account for these errors (and observation noise) within the stochastic model. In this study, an online stochastic modelling technique for network-based GPS RTK positioning is introduced to adaptively estimate the stochastic model in real time. The basis of the method is to utilise the residuals of the previous segment results in order to estimate the stochastic model at the current epoch. Experimental test results indicate that the proposed stochastic modelling technique improves the performance of the least squares estimation and ambiguity resolution. 2004-12-06 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/1169/1/WANG%2C_Jinling_P186.pdf Wang, Jinling and Lee, Hung Kyu and Lee, Young-Jin and Musa, Tajul A. and Rizos, Chris (2004) Online stochastic modelling for network-based GPS real-time kinematic positioning. In: GNSS 2004, 6-8 Dec 2004, The University of New South Wales, Sydney, Australia. http://www.gnss2004.org
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Wang, Jinling
Lee, Hung Kyu
Lee, Young-Jin
Musa, Tajul A.
Rizos, Chris
Online stochastic modelling for network-based GPS real-time kinematic positioning
description Baseline length-dependent errors in GPS RTK positioning, such as orbit uncertainty, and atmospheric effects, constrain the applicable baseline length between reference and mobile user receiver to perhaps 10-15km. This constraint has led to the development of network-based RTK techniques to model such distance-dependent errors. Although these errors can be effectively mitigated by network-based techniques, the residual errors, attributed to imperfect network functional models, in practice, affect the positioning performance. Since it is too difficult for the functional model to define and/or handle the residual errors, an alternative approach that can be used is to account for these errors (and observation noise) within the stochastic model. In this study, an online stochastic modelling technique for network-based GPS RTK positioning is introduced to adaptively estimate the stochastic model in real time. The basis of the method is to utilise the residuals of the previous segment results in order to estimate the stochastic model at the current epoch. Experimental test results indicate that the proposed stochastic modelling technique improves the performance of the least squares estimation and ambiguity resolution.
format Conference or Workshop Item
author Wang, Jinling
Lee, Hung Kyu
Lee, Young-Jin
Musa, Tajul A.
Rizos, Chris
author_facet Wang, Jinling
Lee, Hung Kyu
Lee, Young-Jin
Musa, Tajul A.
Rizos, Chris
author_sort Wang, Jinling
title Online stochastic modelling for network-based GPS real-time kinematic positioning
title_short Online stochastic modelling for network-based GPS real-time kinematic positioning
title_full Online stochastic modelling for network-based GPS real-time kinematic positioning
title_fullStr Online stochastic modelling for network-based GPS real-time kinematic positioning
title_full_unstemmed Online stochastic modelling for network-based GPS real-time kinematic positioning
title_sort online stochastic modelling for network-based gps real-time kinematic positioning
publishDate 2004
url http://eprints.utm.my/id/eprint/1169/1/WANG%2C_Jinling_P186.pdf
http://eprints.utm.my/id/eprint/1169/
http://www.gnss2004.org
_version_ 1643643268262526976
score 13.211869

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