A Novel Aircraft Trajectory Generation Method Embedded with Data Mining - ANITI - Artificial and Natural Intelligence Toulouse Institute
Article Dans Une Revue Aerospace Année : 2024

A Novel Aircraft Trajectory Generation Method Embedded with Data Mining

Résumé

Data mining has achieved great success in air traffic management as a technology for learning knowledge from historical data that benefits people. However, data mining can rarely be embedded into the trajectory optimization process since regular optimization algorithms cannot utilize the functional and implicit knowledge extracted from historical data in a general paradigm. To tackle this issue, this research proposes a novel data mining-based trajectory generation method that is compatible with existing optimization algorithms. Firstly, the proposed method generates trajectories by combining various maneuvers learned from operation data instead of reconstructing trajectories with generative models. In such a manner, data mining-based trajectory optimization can be achieved by solving a combinatorial optimization problem. Secondly, the proposed method introduces a majorization–minimization-based adversarial training paradigm to train the generation model with more general loss functions, including non-differentiable flight performance constraints. A case study on Guangzhou Baiyun International Airport was conducted to validate the proposed method. The results illustrate that the trajectory generation model can generate trajectories with high fidelity, diversity, and flyability.
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Dates et versions

hal-04670021 , version 1 (10-08-2024)

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Xuhao Gui, Junfeng Zhang, Xinmin Tang, Daniel Delahaye, Jie Bao. A Novel Aircraft Trajectory Generation Method Embedded with Data Mining. Aerospace, 2024, 11 (8), ⟨10.3390/aerospace11080648⟩. ⟨hal-04670021⟩
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