Optimizing Global Navigation Satellite Systems network real-time kinematic infrastructure for homogeneous positioning performance from the perspective of tropospheric effects

Abstract

Real-time centimetre-level precise positioning from Global Navigation Satellite Systems (GNSS) is critical for activities including landslide, glacier and coastal erosion monitoring, flood modelling, precision agriculture, intelligent transport systems, autonomous vehicles and the Internet of Things. This may be achieved via the real-time kinematic (RTK) GNSS approach, which uses a single receiver and a network of continuously operating GNSS reference stations (CORS). However, existing CORS networks have often been established simply by attempting regular spacing or in clusters around cities, with little consideration of weather, climate and topography effects, which influence the GNSS tropospheric delay, a substantial GNSS positional error and which prevents homogeneous RTK accuracy attainment. Here, we develop a framework towards optimizing the design of CORS ground infrastructure, such that tropospheric delay errors reduce to 1.5 mm worth of precipitable water vapour (PWV) globally. We obtain average optimal station spacings of 52 km in local summer and 70 km in local winter, inversely related to the atmospheric PWV variation, with denser networks typically required in the tropics and in mountainous areas. We also consider local CORS network infrastructure case studies, showing how after network modification interpolated PWV errors can be reduced from around 2.7 to 1.4 mm.

Reduction of Atmospheric Effects on InSAR Observations Through Incorporation of GACOS and PCA Into Small Baseline Subset InSAR

“…In general, ITD can well reconstruct the mediumto-long-wavelength tropospheric delays as well as topography-dependent tropospheric delays, but shortwavelength topography-independent delays require a denser GNSS network (e.g., [39]). ECMWF tropospheric products are limited by the resolution of assimilation data and, even if they operate in a nested fashion, their products are subject to uncertainties.…”

“…GACOS has proven to be an effective InSAR atmospheric correction method for reducing medium-to-long-wavelength atmospheric effects as well as topography-dependent atmospheric signals [34], [35], [36], [37], [38]. Although GACOS has absorbed some of the GNSS tropospheric delays, the atmosphere at small scales (e.g., 100 m to a few kilometers) remains difficult to estimate and mitigate effectively due to the limited density of stations in the GNSS network used [39]. Since the main data for GACOS come from the numerical simulation model, HRES ECMWF, they also inherit some common disadvantages of numerical simulation models, such as limited spatiotemporal resolutions and seasonal errors.…”