Deep Learning-Based Flight Trajectory Prediction Using Time Series Decomposition

Yue Liu; Jing Sun; Wei Dong; Lele Zhang; Chunyan Wang; Fang Deng

doi:10.1007/978-981-96-2216-0_31

Deep Learning-Based Flight Trajectory Prediction Using Time Series Decomposition

Yue Liu, Jing Sun, Wei Dong^*, Lele Zhang, Chunyan Wang, Fang Deng

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Flight trajectory prediction of unmanned aerial vehicles (UAVs) is essential for air traffic control. Automatic dependent surveillance-broadcast (ADS-B) has been widely used in civil aviation aircraft. However, effective trajectory prediction mechanisms and sufficient data sources for ADS-B information are still lacking specifically for UAVs. This paper proposes a deep learning-based flight trajectory prediction framework using the neural hierarchical interpolation for time series (N-HITS). This framework includes ADS-B flight trajectory data generation, flight trajectory data processing, and N-HITS prediction neural network. Comparative results with existing methods like Gaussian process regression (GPR) and long short-term memory (LSTM) network indicate that the proposed framework has better generalization ability, prediction accuracy, and stability.

Original language	English
Title of host publication	Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 5
Editors	Liang Yan, Haibin Duan, Yimin Deng
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	320-329
Number of pages	10
ISBN (Print)	9789819622153
DOIs	https://doi.org/10.1007/978-981-96-2216-0_31
Publication status	Published - 2025
Event	International Conference on Guidance, Navigation and Control, ICGNC 2024 - Changsha, China Duration: 9 Aug 2024 → 11 Aug 2024

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	1341 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Guidance, Navigation and Control, ICGNC 2024
Country/Territory	China
City	Changsha
Period	9/08/24 → 11/08/24

Keywords

deep learning
flight path prediction
Time series
unmanned aerial vehicle

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10.1007/978-981-96-2216-0_31

Cite this

Liu, Y., Sun, J., Dong, W., Zhang, L., Wang, C., & Deng, F. (2025). Deep Learning-Based Flight Trajectory Prediction Using Time Series Decomposition. In L. Yan, H. Duan, & Y. Deng (Eds.), Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 5 (pp. 320-329). (Lecture Notes in Electrical Engineering; Vol. 1341 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-2216-0_31

Liu, Yue ; Sun, Jing ; Dong, Wei et al. / Deep Learning-Based Flight Trajectory Prediction Using Time Series Decomposition. Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 5. editor / Liang Yan ; Haibin Duan ; Yimin Deng. Springer Science and Business Media Deutschland GmbH, 2025. pp. 320-329 (Lecture Notes in Electrical Engineering).

@inproceedings{41242a1999e04fb693d4a31cf1b05bdb,

title = "Deep Learning-Based Flight Trajectory Prediction Using Time Series Decomposition",

abstract = "Flight trajectory prediction of unmanned aerial vehicles (UAVs) is essential for air traffic control. Automatic dependent surveillance-broadcast (ADS-B) has been widely used in civil aviation aircraft. However, effective trajectory prediction mechanisms and sufficient data sources for ADS-B information are still lacking specifically for UAVs. This paper proposes a deep learning-based flight trajectory prediction framework using the neural hierarchical interpolation for time series (N-HITS). This framework includes ADS-B flight trajectory data generation, flight trajectory data processing, and N-HITS prediction neural network. Comparative results with existing methods like Gaussian process regression (GPR) and long short-term memory (LSTM) network indicate that the proposed framework has better generalization ability, prediction accuracy, and stability.",

keywords = "deep learning, flight path prediction, Time series, unmanned aerial vehicle",

author = "Yue Liu and Jing Sun and Wei Dong and Lele Zhang and Chunyan Wang and Fang Deng",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; International Conference on Guidance, Navigation and Control, ICGNC 2024 ; Conference date: 09-08-2024 Through 11-08-2024",

year = "2025",

doi = "10.1007/978-981-96-2216-0_31",

language = "English",

isbn = "9789819622153",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "320--329",

editor = "Liang Yan and Haibin Duan and Yimin Deng",

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Liu, Y, Sun, J, Dong, W, Zhang, L, Wang, C & Deng, F 2025, Deep Learning-Based Flight Trajectory Prediction Using Time Series Decomposition. in L Yan, H Duan & Y Deng (eds), Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 5. Lecture Notes in Electrical Engineering, vol. 1341 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 320-329, International Conference on Guidance, Navigation and Control, ICGNC 2024, Changsha, China, 9/08/24. https://doi.org/10.1007/978-981-96-2216-0_31

Deep Learning-Based Flight Trajectory Prediction Using Time Series Decomposition. / Liu, Yue; Sun, Jing; Dong, Wei et al.
Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 5. ed. / Liang Yan; Haibin Duan; Yimin Deng. Springer Science and Business Media Deutschland GmbH, 2025. p. 320-329 (Lecture Notes in Electrical Engineering; Vol. 1341 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep Learning-Based Flight Trajectory Prediction Using Time Series Decomposition

AU - Liu, Yue

AU - Sun, Jing

AU - Dong, Wei

AU - Zhang, Lele

AU - Wang, Chunyan

AU - Deng, Fang

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

PY - 2025

Y1 - 2025

N2 - Flight trajectory prediction of unmanned aerial vehicles (UAVs) is essential for air traffic control. Automatic dependent surveillance-broadcast (ADS-B) has been widely used in civil aviation aircraft. However, effective trajectory prediction mechanisms and sufficient data sources for ADS-B information are still lacking specifically for UAVs. This paper proposes a deep learning-based flight trajectory prediction framework using the neural hierarchical interpolation for time series (N-HITS). This framework includes ADS-B flight trajectory data generation, flight trajectory data processing, and N-HITS prediction neural network. Comparative results with existing methods like Gaussian process regression (GPR) and long short-term memory (LSTM) network indicate that the proposed framework has better generalization ability, prediction accuracy, and stability.

AB - Flight trajectory prediction of unmanned aerial vehicles (UAVs) is essential for air traffic control. Automatic dependent surveillance-broadcast (ADS-B) has been widely used in civil aviation aircraft. However, effective trajectory prediction mechanisms and sufficient data sources for ADS-B information are still lacking specifically for UAVs. This paper proposes a deep learning-based flight trajectory prediction framework using the neural hierarchical interpolation for time series (N-HITS). This framework includes ADS-B flight trajectory data generation, flight trajectory data processing, and N-HITS prediction neural network. Comparative results with existing methods like Gaussian process regression (GPR) and long short-term memory (LSTM) network indicate that the proposed framework has better generalization ability, prediction accuracy, and stability.

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KW - Time series

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SN - 9789819622153

T3 - Lecture Notes in Electrical Engineering

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BT - Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 5

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A2 - Duan, Haibin

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Y2 - 9 August 2024 through 11 August 2024

ER -

Liu Y, Sun J, Dong W, Zhang L, Wang C , Deng F. Deep Learning-Based Flight Trajectory Prediction Using Time Series Decomposition. In Yan L, Duan H, Deng Y, editors, Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 5. Springer Science and Business Media Deutschland GmbH. 2025. p. 320-329. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-96-2216-0_31

Deep Learning-Based Flight Trajectory Prediction Using Time Series Decomposition

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