LSTM‑Based Temporal Modeling for ADS‑B Trajectory Prediction: A Comparative Review of Approaches
DOI:
https://doi.org/10.66069/ojspub.26821006Keywords:
ADS‑B trajectory, LSTM, trajectory prediction, spatiotemporal data, deep learningAbstract
Recent advances in artificial intelligence and big data, particularly deep learning architectures such as convolutional neural networks, have enabled new capabilities for processing complex spatiotemporal data. Automatic Dependent Surveillance–Broadcast (ADS‑B) trajectory data, characterized by high‑dimensional spatiotemporal features and inherent uncertainty, present significant challenges for accurate prediction. This study focuses on temporal feature modeling for trajectory prediction, systematically examining various Long Short‑Term Memory (LSTM) network‑based approaches. By comparing different LSTM variants and their effectiveness in capturing temporal dependencies, the paper offers insights into model selection and identifies future research directions for enhancing prediction accuracy in air traffic surveillance and management systems.
References
Li Meng. Aircraft 4D Trajectory Planning Technology under TBO Mode [D]: [Master’s Thesis]. Hebei: North China University of Science and Technology, 2022.
Zhiming Ma, Mengfei Yao, Tao Hong, Bo Li. Aircraft Surface Trajectory Prediction Method Based on LSTM with Attenuated Memory Window[J]. Journal of Physics: Conference Series, 2019, 1215(1):66.
Wang Taobo, Huang Baojun. 4D Flight Trajectory Prediction Model Based on Improved Kalman Filter [J]. Journal of Computer Applications, 2014, 34(6): 1812-1815.
Kastella K, Biscuso M. Tracking algorithms for air traffic control applications[J]. Air Traffic Control Quarterly, 1995, 3(1): 19-43.
Ma Lan, Gao Yongsheng. 4D Trajectory Prediction Method Based on ADS-B Data Mining [J]. Journal of Civil Aviation University of China, 2019, 37(4): 1-4.