Daniel Gerbeth
German Aerospace Center
28 Papers
46 Citations
Daniel Gerbeth is an academic researcher from German Aerospace Center. The author has contributed to research in topics: GNSS augmentation & GNSS applications. The author has an hindex of 5, co-authored 22 publications.
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Papers
Evaluation of GPS L5 and Galileo E1 and E5a Performance for Future Multifrequency and Multiconstellation GBAS
Mihaela-Simona Circiu,Mihaela-Simona Circiu,Michael Meurer,Michael Meurer,Michael Felux,Daniel Gerbeth,Steffen Thölert,Mariano Vergara,Christoph Enneking,Matteo Sgammini,Sam Pullen,Felix Antreich +11 more
TL;DR: In this article, the performance analysis of signals from the Galileo satellites in the E1 and E5a frequency bands and GPS L5 signals as measured by DLR's experimental ground-based augmentation system is presented.
Detection of GNSS Multipath with Time-Differenced Code-Minus-Carrier for Land-Based Applications
Maria Caamano,Omar Garcia Crespillo,Daniel Gerbeth,Anja Grosch +3 more
- 23 Nov 2020
TL;DR: In this paper, the detection of multipath in the code measurements of GNSS receivers for mobile users in urban scenarios is discussed and a practical methodology to design a suitable multipath detector based on the time difference of CMC is proposed.
Optimized Selection of Satellite Subsets for a Multi-Constellation GBAS
Daniel Gerbeth,Michael Felux,Mihaela-Simona Circiu,Maria Caamano +3 more
- 28 Jan 2016
TL;DR: This paper proposes a new satellite selection method which allows fast selection of a variable sized, quasi-optimal subset of all visible GNSS satellites and shows the feasibility of using for instance only 14 satellites in global protection level simulations.
Satellite Selection Methodology for Horizontal Navigation and Integrity Algorithms
Daniel Gerbeth,Ilaria Martini,Markus Rippl,Michael Felux +3 more
- 16 Sep 2016
TL;DR: This paper proposes two different satellites selection strategies adapted for Horizontal ARAIM, a bare geometric approach which comes with almost no additional computation effort at the cost of less stable results and a heuristic optimization which improves selection results significantly while adding additional computational effort.