Temporal correlation for network RTK positioning |
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Authors: | Robert Odolinski |
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Institution: | (1) GNSS Research Centre, Curtin University of Technology, Perth, WA, Australia |
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Abstract: | 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. |
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