Back to Search Start Over

Bidirectional Long Short-Term Memory Development for Aircraft Trajectory Prediction Applications to the UAS-S4 Ehécatl

Authors :
Seyed Mohammad Hashemi
Ruxandra Mihaela Botez
Georges Ghazi
Source :
Aerospace, Vol 11, Iss 8, p 625 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

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.

Details

Language :
English
ISSN :
22264310
Volume :
11
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Aerospace
Publication Type :
Academic Journal
Accession number :
edsdoj.5e7f57e8fb6b48a59bc3a9629690d4d5
Document Type :
article
Full Text :
https://doi.org/10.3390/aerospace11080625