High-quality global navigation satellite systems (GNSS) products are integral to a wide array of scientific and commercial applications. The analysis centers of the International GNSS Service generate such products by processing observations from a global network of ground stations to one or more GNSS constellations. So far, this kind of processing only incorporates elevation-dependent a priori modeling of observation variances and disregards temporal correlations. Meanwhile, numerous studies have shown the positive impact the incorporation of sophisticated stochastic modeling has on GNSS processing and resulting products. The stochastic properties of highly stable atomic clocks onboard GNSS satellites or linked to some receivers can also be modeled in this fashion. While studies show that this improves the resulting GNSS products, global GNSS processing does not yet commonly utilize stochastic modeling of clock estimates.
The main goal of the project is to advance the state of the art of global GNSS processing by incorporating sophisticated stochastic modeling of observation noise and clock estimates. Achieving this goal requires finding the best parametric description of the observation noise covariance matrix and the stochastic properties of clock estimates. Implementing a suitable and efficient normal equation structure enables stochastic modeling even in large-scale global GNSS processing. Furthermore, automatically adjusting the model coefficients by means of variance component estimation is going to result in more realistic noise models. Publishing all findings and developed methodologies on an open-access basis together with a GNSS product time series of at least 10 years benefits the GNSS community and users.
This project is funded within the Austrian Space Applications Program (ASAP) Phase XVI by the Austrian Research Promotion Agency (FFG).
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