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Neural Network-Based Aircraft Conflict Prediction in Final Approach Maneuvers

Computing Systems Department, Universidad de Castilla–La Mancha (UCLM), Campus Universitario, s/n, 02071 Albacete, Spain
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Electronics 2020, 9(10), 1708; https://doi.org/10.3390/electronics9101708
Received: 1 October 2020 / Revised: 14 October 2020 / Accepted: 15 October 2020 / Published: 18 October 2020
(This article belongs to the Special Issue Autonomous Navigation Systems: Design, Control and Applications)
Conflict detection and resolution is one of the main topics in air traffic management. Traditional approaches to this problem use all the available information to predict future aircraft trajectories. In this work, we propose the use of a neural network to determine whether a particular configuration of aircraft in the final approach phase will break the minimum separation requirements established by aviation rules. To achieve this, the network must be effectively trained with a large enough database, in which configurations are labeled as leading to conflict or not. We detail the way in which this training database has been obtained and the subsequent neural network design and training process. Results show that a simple network can provide a high accuracy, and therefore, we consider that it may be the basis of a useful decision support tool for both air traffic controllers and airborne autonomous navigation systems. View Full-Text
Keywords: autonomous air navigation; air traffic management (ATM); conflict/collision detection and resolution (CD and R); neural networks; multi-layer perceptron (MLP) autonomous air navigation; air traffic management (ATM); conflict/collision detection and resolution (CD and R); neural networks; multi-layer perceptron (MLP)
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MDPI and ACS Style

Casado, R.; Bermúdez, A. Neural Network-Based Aircraft Conflict Prediction in Final Approach Maneuvers. Electronics 2020, 9, 1708.

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