Improvements in Aircraft Final Approach Procedures

The main aim of this research is to develop different avionics navigation systems that will allow to the aircrafts performing approach and landing maneuvers in a more efficient way. These systems will be supported by state-of-the-art navigation technologies, such as avian radars, GBAS, and ADS-B, and by the new context provided by Performance-Based Navigation (PBN). Our developments can be employed to avoid bird strikes, to improve the usual missed approach procedure, or to efficiently handle aircraft traffic flows, for example, to increase airport capacity.


As an alternative implementation for these systems, we are considering the employ of deep learning techniques, based on neural networks. Once it has been trained, the neural network will be able to provide the aircrafts with the paths to follow, replacing in this way the ATC (air traffic controller) function.


With respect our framework, we employ the Matlab/Simulink software for modeling and simulating the airspace and the final approach procedure. This work environment allows us to design and to test our developments.




  • Air navigation systems
  • Performance-Based Navigation
  • Risk management
  • Deep learning and neural networks


Relevant Publications:

  • Manuel Lopez-Lago, Rafael Casado, Aurelio Bermudez, Jose Serna
    A predictive model for risk assessment on imminent bird strikes on airport areas
    Aerospace Science and Technology, Volume 62, March 2017 DOI: 10.1016/j.ast.2016.11.020
  • Aurelio Bermúdez, Rafael Casado, Guillermo Fernández, María Guijarro, Pablo Olivas
    Drone challenge: A platform for promoting programming and robotics skills in K-12 education
    International Journal of Advanced Robotic Systems, Volume 16, Issue 1, January 2019 DOI: 10.1177/1729881418820425


Rafael Casado Rafael Casado, PhD
Associate Professor
Phone number: +34 967 599200 – Ext. 2279
Email: iD icon dblp.icon.18x18
abermu Aurelio Bermúdez, PhD
Associate Professor
Phone number: +34 967 599 200 – Ext. 2551
Email: iD icon dblp.icon.18x18