Approach and landing aircraft on-board parameters estimation with LSTM networks

Gabriel Jarry (1) , Daniel Delahaye (1) , Eric Féron (2)

1 ENAC - Ecole Nationale de l'Aviation Civile
2 Georgia Institute of Technology [Atlanta]

This paper addresses the problem of estimating aircraft on-board parameters using ground surveillance available parameters. The proposed methodology consists in training supervised Neural Networks with Flight Data Records to estimate target parameters. This paper investigates the learning process upon three case study parameters: the fuel flow rate, the flap configuration, and the landing gear position. Particular attention is directed to the generalization to different aircraft types and airport approaches. From the Air Traffic Management point of view, these additional parameters enable a better understanding and awareness of aircraft behaviors. These estimations can be used to evaluate and enhance the air traffic management system performance in terms of safety and efficiency.

Mots clés: Learning Generalization; Long Short-Term Memory; Machine Learning; Flight Data Monitoring; Neural Networks; Aircraft Engine Fuel Flow Rate; Supervised Learning

Domaines: Machine Learning [stat.ML]; Intelligence artificielle [cs.AI]; Optimisation et contrôle [math.OC]; Réseau de neurones [cs.NE]

Gabriel Jarry, Daniel Delahaye, Eric Féron. Approach and landing aircraft on-board parameters estimation with LSTM networks. AIDA-AT 2020, 1st conference on Artificial Intelligence and Data Analytics in Air Transportation, Feb 2020, Singapore, Singapore. pp.ISBN: 978-1-7281-5381-0, ⟨10.1109/AIDA-AT48540.2020.9049199⟩. ⟨hal-02506741⟩