Temporal correlation for network RTK positioning
Temporal correlation in network real-time kinematic (RTK) data exists due to unmodeled multipath and atmospheric errors, in combination with slowly changing satellite constellation. If this correlation is neglected, the estimated uncertainty of the coordinates might be too optimistic. In this study,...
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| Format: | Journal Article |
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Springer
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/30227 |
| _version_ | 1848753027924623360 |
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| author | Odolinski, Robert |
| author_facet | Odolinski, Robert |
| author_sort | Odolinski, Robert |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Temporal correlation in network real-time kinematic (RTK) data exists due to unmodeled multipath and atmospheric errors, in combination with slowly changing satellite constellation. If this correlation is neglected, the estimated uncertainty of the coordinates might be too optimistic. In this study, we compute temporal correlation lengths for network RTK positioning, i.e., the appropriate time separation between the measurements. This leads to more realistic coordinate uncertainty estimates, and an appropriate surveying strategy to control the measurements can be designed. Two methods to estimate temporal correlation lengths are suggested. Several monitor stations that utilize correction data from two SWEPOSTM Network RTK services, a standard service and a project-adapted service with the mean distance between the reference stations of approximately 70 and 10-20 km, are evaluated. The correlation lengths for the standard service are estimated as 17 min for the horizontal component and 36-37 min for the vertical component. The corresponding estimates for the project-adapted service are 13-17 and 13-16 min, respectively. According to the F test, the proposed composite first-order Gauss--Markov autocovariance function shows a significantly better least-squared fit to data compared to the commonly used one-component first-order Gauss-Markov model. A second suggested method is proposed that has the potential of providing robust correlation lengths without the need to fit a model to the computed autocovariance function. |
| first_indexed | 2025-11-14T08:18:00Z |
| format | Journal Article |
| id | curtin-20.500.11937-30227 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:18:00Z |
| publishDate | 2012 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-302272017-09-13T16:07:46Z Temporal correlation for network RTK positioning Odolinski, Robert Temporal correlation in network real-time kinematic (RTK) data exists due to unmodeled multipath and atmospheric errors, in combination with slowly changing satellite constellation. If this correlation is neglected, the estimated uncertainty of the coordinates might be too optimistic. In this study, we compute temporal correlation lengths for network RTK positioning, i.e., the appropriate time separation between the measurements. This leads to more realistic coordinate uncertainty estimates, and an appropriate surveying strategy to control the measurements can be designed. Two methods to estimate temporal correlation lengths are suggested. Several monitor stations that utilize correction data from two SWEPOSTM Network RTK services, a standard service and a project-adapted service with the mean distance between the reference stations of approximately 70 and 10-20 km, are evaluated. The correlation lengths for the standard service are estimated as 17 min for the horizontal component and 36-37 min for the vertical component. The corresponding estimates for the project-adapted service are 13-17 and 13-16 min, respectively. According to the F test, the proposed composite first-order Gauss--Markov autocovariance function shows a significantly better least-squared fit to data compared to the commonly used one-component first-order Gauss-Markov model. A second suggested method is proposed that has the potential of providing robust correlation lengths without the need to fit a model to the computed autocovariance function. 2012 Journal Article http://hdl.handle.net/20.500.11937/30227 10.1007/s10291-011-0213-0 Springer restricted |
| spellingShingle | Odolinski, Robert Temporal correlation for network RTK positioning |
| title | Temporal correlation for network RTK positioning |
| title_full | Temporal correlation for network RTK positioning |
| title_fullStr | Temporal correlation for network RTK positioning |
| title_full_unstemmed | Temporal correlation for network RTK positioning |
| title_short | Temporal correlation for network RTK positioning |
| title_sort | temporal correlation for network rtk positioning |
| url | http://hdl.handle.net/20.500.11937/30227 |