Hybrid Survey Networks: Combining Real-Time and Static GNSS Observations for Optimizing Height Modernization

Abstract

To derive ellipsoid heights on passive marks with centimeter-level accuracy, many current specifications require the collection and adjustment of long-duration, static, postprocessed global navigation satellite system (GNSS) sessions. To increase efficiency, a campaign-style survey procedure that includes real-time kinematic (RTK) vectors from a real-time GNSS network was evaluated. Thirty different hybrid networks involving three to nine network RTK (NRTK) vectors per mark and some static GNSS vectors were developed from surveys completed in Oregon and South Carolina. The variance-covariance matrices of the static and kinematic vectors were scaled by variance-component estimation procedures to produce realistic error estimates for stochastic modeling. After least-squares adjustment and formal random-error propagation of the networks, the resulting ellipsoid heights on the passive marks had network accuracies ranging from .6 to 3.6 cm (95% confidence). These network accuracies reduced to < 2 cm when using six or more NRTK observations per mark. Further, the use of NRTK vectors obtained from observables of both the U.S. global positioning system (GPS) and Russia’s GNSS (GLONASS) were, on average, 19.2% more accurate vertically than vectors obtained solely from GPS observables.