479.575 PEOPLE
| People | Locations | Statistics |
|---|---|---|
| Ziakopoulos, Apostolos | Athens |
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| Vigliani, Alessandro | Turin |
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| Catani, Jacopo | Rome |
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| Statheros, Thomas | Stevenage |
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| Utriainen, Roni | Tampere |
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| Guglieri, Giorgio | Turin |
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| Martínez Sánchez, Joaquín |
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| Tobolar, Jakub |
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| Volodarets, M. |
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| Piwowar, Piotr |
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| Tennoy, Aud | Oslo |
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| Matos, Ana Rita |
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| Cicevic, Svetlana |
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| Sommer, Carsten | Kassel |
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| Liu, Meiqi |
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| Pirdavani, Ali | Hasselt |
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| Niklaß, Malte |
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| Lima, Pedro | Braga |
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| Turunen, Anu W. |
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| Antunes, Carlos Henggeler |
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| Krasnov, Oleg A. |
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| Lopes, Joao P. |
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| Turan, Osman |
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| Lučanin, Vojkan | Belgrade |
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| Tanaskovic, Jovan |
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Felux, Michael
in Cooperation with on an Cooperation-Score of 37%
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Publications (35/35 displayed)
- 2023Analysis of GNSS disruptions in European airspace
- 2022GNSS Jamming and Its Effect on Air Traffic in Eastern Europe
- 2022GBAS use cases beyond what was envisioned – drone navigation
- 2022Flight testing GBAS for UAV operations
- 2022Airborne Ionospheric Gradient Monitoring for Dual-Frequency GBAS
- 2022A standardizeable framework enabling DME/DME to support RNP
- 2022Impact of GNSS-band radio interference on operational avionics
- 2022Identification and operational impact analysis of GNSS RFI based on flight crew reports and ADS-B data
- 2022Impact of GNSS outage on mid-air collision
- 2021Flight trial demonstration of secure GBAS via the L-band digital aeronautical communications system (LDACS)citations
- 2021Final results on airborne multipath models for dualconstellation dual-frequency aviation applications
- 2021Impact of RFI on GNSS and avionics : a view from the cockpitcitations
- 2021Network-based ionospheric gradient monitoring to support GBAScitations
- 2021Flight Trial Demonstration of Secure GBAS via the L-band Digital Aeronautical Communication System (LDACS)citations
- 2020Combined Multilateration with Machine Learning for Enhanced Aircraft Localizationcitations
- 2020Network-Based Ionospheric Gradient Monitoring to Support GBAS
- 2019Towards Airborne Multipath Models for Dual Constellation and Dual Frequency GNSScitations
- 2019Initial results for dual constellation dual-frequency multipath models
- 2018Total System Performance of GBAS-based Automatic Landings ; Leistungsfähigkeit des Gesamtsystems GBAS-basierter Automatischer Landungen
- 2018Transmitting GBAS messages via LDACS
- 2018Total System Performance of GBAS-based Automatic Landings
- 2017Ionospheric Gradient Threat Mitigation in Future Dual Frequency GBAScitations
- 2017Future Dual Frequency Multi Constellation GBAS
- 2017Using a Wide Area Receiver Network to Support GBAS Ionospheric Monitoring
- 2017Future GBAS Processing - Do we need an ionosphere-free mode?
- 2016Multi-constellation GBAS: how to benefit from a second constellation
- 2015GBAS Ground Monitoring Requirements from an Airworthiness Perspectivecitations
- 2015Total System Performance in GBAS-based Landings
- 2013GBAS Approach Guidance Performance – A comparison to ILS
- 2012Approach service type D evaluation of the DLR GBAS testbedcitations
- 2012Flight Testing the GAST D Solution at DLR's GBAS Test Bed
- 2011Approach service type D evaluation of the DLR GBAS testbedcitations
- 2011Evaluation of GBAS Flight Tests with respect to GAST-D Requirements
- 2011GAST-D Monitoring Results from Post-processed Flight Trial Data - Performance Evaluation of DLR´s GBAS Testbed
- 2009A Robust and Effective GNSS/INS Integration Optimizing Cost and Effort
Places of action
Abstract
Large ionospheric gradients acting between a Ground Based Augmentation System (GBAS) reference station and an aircraft on approach could lead to hazardous position errors if undetected. Current GBAS stations provide solutions against this threat that rely on the use of “worst‐case” conservative threat models, which could limit the availability of the system. This paper presents a methodology capable of detecting ionospheric gradients in real time and estimating the actual threat model parameters based on a network of dual‐frequency and multi‐constellation GNSS monitoring stations. First, we evaluate the performance of our algorithm with synthetic gradients that are simulated over the nominal measurements recorded by a reference network in Alaska. Afterwards, we also assess it with one real ionospheric gradient measured by the same network. Results with both simulated gradients and a real gradient show the potential to support GBAS by detecting and estimating these gradients instead of always using “worst‐case” models.
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Network-based ionospheric gradient monitoring to support GBAS