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...
| Main Authors: | , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2004
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/1169/1/WANG%2C_Jinling_P186.pdf |
| _version_ | 1796853196369428480 |
|---|---|
| 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 |
| collection | ePrints |
| 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. |
| first_indexed | 2024-03-05T17:56:00Z |
| format | Conference or Workshop Item |
| id | utm.eprints-1169 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T17:56:00Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | utm.eprints-11692017-08-30T07:36:16Z http://eprints.utm.my/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/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 |
| 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 |
| title | 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_short | Online stochastic modelling for network-based GPS real-time kinematic positioning |
| title_sort | online stochastic modelling for network based gps real time kinematic positioning |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.utm.my/1169/1/WANG%2C_Jinling_P186.pdf |
| work_keys_str_mv | AT wangjinling onlinestochasticmodellingfornetworkbasedgpsrealtimekinematicpositioning AT leehungkyu onlinestochasticmodellingfornetworkbasedgpsrealtimekinematicpositioning AT leeyoungjin onlinestochasticmodellingfornetworkbasedgpsrealtimekinematicpositioning AT musatajula onlinestochasticmodellingfornetworkbasedgpsrealtimekinematicpositioning AT rizoschris onlinestochasticmodellingfornetworkbasedgpsrealtimekinematicpositioning |