Hashemi, Seyed Mohammad, Botez, Ruxandra Mihaela et Ghazi, Georges.
2024.
« Bidirectional long short-term memory development for aircraft trajectory prediction applications to the UAS-S4 Ehécatl ».
Aerospace, vol. 11, nº 8.
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Résumé
The rapid advancement of unmanned aerial systems in various civilian roles necessitates improved safety measures during their operation. A key aspect of enhancing safety is effective collision avoidance, which is based on conflict detection and is greatly aided by accurate trajectory prediction. This paper represents a novel data-driven trajectory prediction methodology based on applying the Long Short-Term Memory (LSTM) prediction algorithm to the UAS-S4 Ehécatl. An LSTM model was designed as the baseline and then developed into a Staked LSTM to better capture complex and hierarchical temporal trajectory patterns. Next, the Bidirectional LSTM was developed for a better understanding of the contextual trajectories from both its past and future data points, and to provide a more comprehensive temporal perspective that could enhance its accuracy. LSTM-based models were evaluated in terms of mean absolute percentage errors. The results reveal the superiority of the Bidirectional LSTM, as it could predict UAS-S4 trajectories more accurately than the Stacked LSTM. Moreover, the developed Bidirectional LSTM was compared with other state-of-the-art deep neural networks aimed at aircraft trajectory prediction. Promising results confirmed that Bidirectional LSTM exhibits the most stable MAPE across all prediction horizons.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Botez, Ruxandra Ghazi, Georges |
| Affiliation: | Génie des systèmes, Génie des systèmes |
| Date de dépôt: | 20 sept. 2024 18:20 |
| Dernière modification: | 28 oct. 2024 15:23 |
| URI: | https://espace2.etsmtl.ca/id/eprint/29542 |
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