"The Aviation Infrastructure team focuses on the latest technologies and processes that ensure safe and sustainable air transport. With the development of new technologies, an important contribution can be made to reducing emissions (both globally and in the vicinity of airports) while maintaining the highest level of safety."
Dr. Michael Felux, Head of research unit Aviation Infrastructure
The research unit
The Aviation Infrastructure team deals with infrastructure in the context of aviation. This includes, for example, navigation systems such as the ground-based instrument landing system ILS or the global satellite navigation system GNSS.
In order to improve safety and sustainability in aviation, noise- and emission-optimized approach procedures using satellite navigation are investigated and simulations for assessing the safety of flight procedures (Collision Risk Modeling, CRM) are performed and analyzed. The use of satellite navigation procedures and supplementary systems is not only applied in the context of classical aviation but also in new application areas such as UAV navigation.
Another research focus is the investigation of the impact of jamming on air traffic. In this context, research is being conducted on robust Communications, Navigation, Surveillance (CNS) procedures to reduce the influence of jamming.
The following types of projects are carried out in the research unit Aviation Infrastructure:
- HISU: Use of the GBAS corrections from Zurich Airport for UAV navigation.
- HISU-HAS: Accurate and reliable navigation using Galilleo's High Accuracy Service.
- CRM: Assessment of the safety of flight procedures.
- ERSNAP: Reduction of emissions by using satellite navigation during the final approach.
- Generic Collision Avoidance Algorithm.
- Large landing trajectory data set for go-around analysis:
Monstein, R., Figuet, B., Krauth, T., Waltert, M., & Dettling, M. (2022, December). Large Landing Trajectory Dataset for Go-Around Analysis. Engineering Proceedings, 28(1), 2
- Localization of interrogators with a 1030/1090 MHz spectrum monitoring system:
Sarperi, L., Jäger, M., Müller, P., & Felux, M. (2022, November). Localization of interrogators with a 1030/1090 MHz spectrum monitoring system. In 33rd Congress of the International Council of the Aeronautical Sciences (ICAS), Stockholm, Sweden, 4-9 September 2022. International Council of the Aeronautical Sciences
- Impact of GNSS outage on mid-air collision:
Figuet, B., Waltert, M., Monstein, R., & Felux, M. (2022, October). Impact of GNSS outage on mid-air collision risk. In Proceedings of International Workshop on ATM/CNS 2022 International Workshop on ATM/CNS (pp. 41-48). Electronic Navigation Research Institute
- Flight Testing GBAS for UAV Operations:
Felux, M., Jochems, S., Schnüriger, P., Fischer, V., Steiner, P., Jäger, M., ... & Cacciopoli, N. (2022, September). Flight testing GBAS for UAV operations. In Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022) (pp. 1576-1588)
- GBAS use cases beyond what was envisioned - drone navigation:
Jochems, S., Felux, M., Schnüriger, P., Jäger, M., & Sarperi, L. (2022, January). GBAS use cases beyond what was envisioned–drone navigation. In Proceedings of the 2022 International Technical Meeting of The Institute of Navigation (pp. 310-320)
- Synthetic aircraft trajectories generated with multivariate density models:
Krauth, T., Morio, J., Olive, X., Figuet, B., & Monstein, R. (2021, December). Synthetic aircraft trajectories generated with multivariate density models. Engineering Proceedings, 13(1), 7
- Ratio-based design hour determination for airport passenger terminal facilities:
Waltert, M., Wicki, J., Perez, E. J., & Pagliari, R. (2021, September). Ratio-based design hour determination for airport passenger terminal facilities. Journal of Air Transport Management, 96, 102125
- Predicting airplane go-arounds using machine learning and open-source data:
Figuet, B., Monstein, R., Waltert, M., & Barry, S. (2020, December). Predicting airplane go-arounds using machine learning and open-source data. In Proceedings (Vol. 59, No. 1, p. 6). MDPI
- Combined multilateration with machine learning for enhanced aircraft localization:
Figuet, B., Monstein, R., & Felux, M. (2020, December). Combined Multilateration with Machine Learning for Enhanced Aircraft Localization. In Proceedings (Vol. 60, No. 1, p. 2). MDPI