MAP - MAchine learning based Plasma density model
Satellite technology and in particular GPS- or GNSS-based systems are becoming vital for our society. Plasma density structures in the near-Earth space can significantly influence the propagation of GPS signals and hence influence the accuracy of GPS navigation. Moreover, space plasmas can also damage satellites. To carefully evaluate these effects of the space environment, it is important to develop an accurate model of the plasma density based on a variety of direct and indirect measurements.
In this pilot-project funded by the Helmholtz Association's Incubator Project and in conjunction with the German Aerospace Centre's Institute of Data Science in Jena and Institute of Communications and Navigation in Neustrelitz, we are demonstrating how machine learning tools can be used to produce a real-time global empirical model of the near-Earth plasma density based on a variety of measurements. Further development of models borne out of this pilot-project will then be able to utilize all available data for use by a broad range of stakeholders for GPS navigation and satellite operations.
Dec 2019 – Aug 2023
Helmholtz Incubator Information & Data Science Pilot-Project
- Prof. Yuri Shprits (GFZ)
- Dr. Jens Berdermann (DLR Neustrelitz)
- Dr. Marcus Paradies (DLR Jena)
- Prof. Yuri Shprits
- Dr. Ruggero Vasile
- Dr. Irina Zhelavskaya
- Artem Smirnov
- Karolina Kume
- Deutsches Zentrum für Luft- und Raumfahrt, Institute of Communications and Navigation (DLR Neustrelitz)
- Deutsches Zentrum für Luft- und Raumfahrt, Institute of Data Science (DLR Jena)