Efficient multi-GNSS processing based on raw observations from large global station networks

The year 2020 is going to mark the first time of four global navigation satellite systems (i.e., GPS, GLONASS, Galileo, and BeiDou) in full operational capability. Utilizing the various available observation types together in global multi-GNSS processing offers new opportunities, but also poses many challenges. The raw observation approach facilitates the incorporation of any undifferenced and uncombined code and phase observation on any frequency into a combined least squares adjustment. Due to the increased number of observation equations and unknown parameters, using raw observations directly is more computationally demanding than using, for example, ionosphere-free double-differenced observations. This is especially relevant for our contribution to the third reprocessing campaign of the International GNSS Service, where we process observations from up to 800 stations per day to three GNSS constellations at a 30-second sampling. For a single day, this results in more than 200 million raw observations, from which we estimate almost 5 million parameters.

Processing such a large number of raw observations together is computationally challenging and requires a highly optimized processing chain. In this contribution, we detail the key steps that make such a processing feasible in the context of a distributed computing environment (i.e., large computer clusters). Some of these steps are the efficient setup of observation equations, a suitable normal equation structure, a sophisticated integer ambiguity resolution scheme, automatic outlier downweighting based on variance component estimation, and considerations regarding the estimability of certain parameter groups.