Simulation case study of displacement monitoring using network derived positioning
Network-based real-time kinematic (NRTK) GNSS (Global Navigation Satellite System) is among the most commonly used surveying technique in many countries for various applications that requires a single GNSS receiver. The main principle of this system is to generate reliable error models that can miti...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-01-01
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| Series: | Geomatics, Natural Hazards & Risk |
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| Online Access: | http://dx.doi.org/10.1080/19475705.2020.1772382 |
| _version_ | 1818611964614541312 |
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| author | Sermet Ogutcu |
| author_facet | Sermet Ogutcu |
| author_sort | Sermet Ogutcu |
| collection | DOAJ |
| description | Network-based real-time kinematic (NRTK) GNSS (Global Navigation Satellite System) is among the most commonly used surveying technique in many countries for various applications that requires a single GNSS receiver. The main principle of this system is to generate reliable error models that can mitigate dispersive (e.g. ionospheric delay) and non-dispersive (e.g. tropospheric delay and orbit bias) errors, which are the main sources of the degradation of positioning accuracy. In this study, the performance of NRTK positioning for deformation and landslide monitoring is investigated using a simulation apparatus. For this purpose, 20 simulated two-dimensional and vertical displacements are conducted within a two-month period in 2019. The 24-h NRTK data with 1-s sampling intervals are obtained using a CHC N72 GNSS reference receiver for each displacement. The 24-h NRTK data are mutually subdivided into 12-, 6-, 3- and 1-h non-overlapping sessions to investigate the effect of observation time on monitoring displacement performance. Two filtering methods – namely, averaging the raw observations and averaging the observations derived from Kalman filtering – are chosen for each session. The displacements obtained from the filtered NRTK observations are compared to the simulated (true) displacements. Another experiment is carried out to monitor the displacement in real time using first-order low-pass and moving-average filters. The NRTK data are collected during the displacement for a real-time experiment. The results of the experiments indicate that 1-sigma horizontal and vertical Root Mean Square errors (RMSEs) of displacements between the filtered NRTK data and true displacements are determined to be 1.5 and 5.4 mm, respectively, using 24-h data. The results also suggest that no significant difference is observed between the filtering methods for most of the points. The RMSE of the two-dimensional displacement direction between the azimuth angle derived from the filtered NRTK data and the azimuth angle of true direction is found to be 15° for 3 mm displacement using 24-h data. For detecting the displacements visually in real time, the minimum magnitude of displacements needs to be 7 mm and 10 mm for the horizontal and vertical components, respectively, to distinguish the displacements from the noise. |
| first_indexed | 2024-12-16T15:38:42Z |
| format | Article |
| id | doaj.art-5bd254aa78544fa09498d0922b243028 |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-12-16T15:38:42Z |
| publishDate | 2020-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-5bd254aa78544fa09498d0922b2430282022-12-21T22:26:05ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132020-01-011111031105110.1080/19475705.2020.17723821772382Simulation case study of displacement monitoring using network derived positioningSermet Ogutcu0Department of Surveying Engineering, Necmettin Erbakan UniversityNetwork-based real-time kinematic (NRTK) GNSS (Global Navigation Satellite System) is among the most commonly used surveying technique in many countries for various applications that requires a single GNSS receiver. The main principle of this system is to generate reliable error models that can mitigate dispersive (e.g. ionospheric delay) and non-dispersive (e.g. tropospheric delay and orbit bias) errors, which are the main sources of the degradation of positioning accuracy. In this study, the performance of NRTK positioning for deformation and landslide monitoring is investigated using a simulation apparatus. For this purpose, 20 simulated two-dimensional and vertical displacements are conducted within a two-month period in 2019. The 24-h NRTK data with 1-s sampling intervals are obtained using a CHC N72 GNSS reference receiver for each displacement. The 24-h NRTK data are mutually subdivided into 12-, 6-, 3- and 1-h non-overlapping sessions to investigate the effect of observation time on monitoring displacement performance. Two filtering methods – namely, averaging the raw observations and averaging the observations derived from Kalman filtering – are chosen for each session. The displacements obtained from the filtered NRTK observations are compared to the simulated (true) displacements. Another experiment is carried out to monitor the displacement in real time using first-order low-pass and moving-average filters. The NRTK data are collected during the displacement for a real-time experiment. The results of the experiments indicate that 1-sigma horizontal and vertical Root Mean Square errors (RMSEs) of displacements between the filtered NRTK data and true displacements are determined to be 1.5 and 5.4 mm, respectively, using 24-h data. The results also suggest that no significant difference is observed between the filtering methods for most of the points. The RMSE of the two-dimensional displacement direction between the azimuth angle derived from the filtered NRTK data and the azimuth angle of true direction is found to be 15° for 3 mm displacement using 24-h data. For detecting the displacements visually in real time, the minimum magnitude of displacements needs to be 7 mm and 10 mm for the horizontal and vertical components, respectively, to distinguish the displacements from the noise.http://dx.doi.org/10.1080/19475705.2020.1772382displacementnrtkglonassgps |
| spellingShingle | Sermet Ogutcu Simulation case study of displacement monitoring using network derived positioning Geomatics, Natural Hazards & Risk displacement nrtk glonass gps |
| title | Simulation case study of displacement monitoring using network derived positioning |
| title_full | Simulation case study of displacement monitoring using network derived positioning |
| title_fullStr | Simulation case study of displacement monitoring using network derived positioning |
| title_full_unstemmed | Simulation case study of displacement monitoring using network derived positioning |
| title_short | Simulation case study of displacement monitoring using network derived positioning |
| title_sort | simulation case study of displacement monitoring using network derived positioning |
| topic | displacement nrtk glonass gps |
| url | http://dx.doi.org/10.1080/19475705.2020.1772382 |
| work_keys_str_mv | AT sermetogutcu simulationcasestudyofdisplacementmonitoringusingnetworkderivedpositioning |