Assessment of GPS-based accelerometry performance with adaptive filter settings

High-quality GPS observations from low Earth orbiting (LEO) satellites can be used to derive thermosphere densities along the satellite orbit. Such GPS-derived densities are currently computed operationally for all three Swarm satellites, in the framework of the Swarm Data, Innovation, and Science Cluster. Considering the increasing number of LEO satellites equipped with GPS receivers, this so-called GPS-based accelerometry approach offers great potential for improving thermosphere models and for studying the influence of solar and geomagnetic activity on the thermosphere.

To better quantify the accuracy that can be obtained with this approach, we assess the performance using the GRACE mission as a test case. For this mission high quality accelerometer data are available, which we can use to validate our GPS-based results. In addition, the GRACE mission has experienced a large variation in density signals, which allows us to assess the performance under a large range of conditions.

We present our GPS-based accelerometry processing strategy, which is based on a Kalman filter approach. The radiation pressure accelerations are accurately modelled and empirical accelerations capture the remaining aerodynamic signal. The empirical accelerations are modeled as Gauss-Markov processes defined by a steady-state variance, process noise and correlation time, which require careful tuning. This applies in particular to the setting of the process noise in the along-track direction, due to the large variations in the encountered aerodynamic signal. Best performance is obtained when the process noise setting is adapted to these variations. Using these adaptive filter settings, results are shown for periods with low, moderate, and high density. In a next step, we will implement the improved filter settings into our regular Swarm GPS-derived density processing chain.