COSMOS: Incorporating stochastic and clock modeling into global multi-GNSS processing

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.


During this project, our group contributed to the third reprocessing campaign (repro3) of the International GNSS Service (IGS). The resulting 27-year time series of GNSS products can be found on the download page.


This project is funded within the Austrian Space Applications Program (ASAP) Phase XVI by the Austrian Research Promotion Agency (FFG).


Dumitraschkewitz, P., Strasser, S., & Mayer-Guerr, T. (2022). Empirical stochastic modeling of observation noise in global GNSS network processing. Abstract from EGU General Assembly 2022, Virtuell, Austria.

Strasser, S., Mayer-Guerr, T., Suesser-Rechberger, B., & Dumitraschkewitz, P. (2022). Estimable phase and code biases in the frame of global multi-GNSS processing. Abstract from EGU General Assembly 2022, Virtuell, Austria.

Suesser-Rechberger, B., Krauss, S., Strasser, S., & Mayer-Guerr, T. (2022). Improved precise kinematic LEO orbits based on the raw observation approach. Advances in Space Research, 69(10), 3559-3570.

Strasser, S. (2022). Reprocessing Multiple GNSS Constellations and a Global Station Network from 1994 to 2020 with the Raw Observation Approach. Doctoral Thesis. Verlag der Technischen Universität Graz.

Navarro Trastoy, A., Strasser, S., Tuppi, L., Vasiuta, M., Poutanen, M., Mayer-Gürr, T., & Järvinen, H. (2022). Coupling a weather model directly to GNSS orbit determination – case studies with OpenIFS. Geoscientific Model Development, 15(7), 2763-2771.

Massarweh, L., Strasser, S., & Mayer-Gürr, T. (2021). On vectorial integer bootstrapping implementations in the estimation of satellite orbits and clocks based on small global networks. Advances in Space Research, 68(11), 4303-4320.

Loyer, S., Banville, S., Geng, J., & Strasser, S. (2021). Exchanging satellite attitude quaternions for improved GNSS data processing consistency. Advances in Space Research, 68(6), 2441-2452.

Mayer-Gürr, T., Behzadpour, S., Eicker, A., Ellmer, M., Koch, B., Krauss, S., Pock, C., Rieser, D., Strasser, S., Suesser-Rechberger, B., Zehentner, N., Kvas, A. (2021). GROOPS: A software toolkit for gravity field recovery and GNSS processing. Computers & Geosciences, 104864.

Strasser, S., Banville, S., Kvas, A., Loyer, S., and Mayer-Gürr, T. (2021). Comparison and generalization of GNSS satellite attitude models. vPICO presented at EGU General Assembly 2021, virtual, Austria.

Strasser, S. and Mayer-Gürr, T. (2021) IGS repro3 products by Graz University of Technology (TUG). Data set. Graz University of Technology.

Strasser, S. and Mayer-Gürr, T. (2020). The IGS Repro3 Contribution by TU Graz: Benefits and Challenges of an Uncombined and Undifferenced Processing Approach. Poster presented at AGU Fall Meeting 2020, virtual, USA.

Strasser, S. and Mayer-Gürr, T. (2020). Efficient multi-GNSS processing based on raw observations from large global station networks. vPICO presented at EGU General Assembly 2020, virtual, Austria.

Glaner, M., Weber, R., and Strasser, S. (2020). PPP-AR with GPS and Galileo: Assessing diverse approaches and satellite products to reduce convergence time. vPICO presented at EGU General Assembly 2020, virtual, Austria.

Banville, S., Geng, J., Loyer, S., Schaer, S., Springer, T., and Strasser, S. (2020). On the interoperability of IGS products for precise point positioning with ambiguity resolution. J Geod 94(1), 10.

Strasser, S., Mayer-Gürr, T., and Zehentner, N. (2019) Processing of GNSS constellations and ground station networks using the raw observation approach. J Geod 93, 1045–1057.


Barbara Suesser-Rechberger
Steyrergasse 30/III
8010 Graz
Tel: +43/316/873- 6347
Fax: +43/316/873-6845

Torsten Mayer-Gürr
Steyrergasse 30/III
8010 Graz
Tel: +43 316 873-6359
Fax: +43 316 873-6845