Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace
For air traffic management, trajectory prediction plays an important role as the predicted trajectory information is used in crucial tasks for the safety and efficiency of air traffic operations, such as conflict detection and resolution, scheduling, and sequencing. In this paper, we propose a frame...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9605684/ |
| _version_ | 1818402122963615744 |
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| author | Hong-Cheol Choi Chuhao Deng Inseok Hwang |
| author_facet | Hong-Cheol Choi Chuhao Deng Inseok Hwang |
| author_sort | Hong-Cheol Choi |
| collection | DOAJ |
| description | For air traffic management, trajectory prediction plays an important role as the predicted trajectory information is used in crucial tasks for the safety and efficiency of air traffic operations, such as conflict detection and resolution, scheduling, and sequencing. In this paper, we propose a framework for trajectory prediction in terminal airspace by combining a machine learning-based method and a physics-based estimation method. A trajectory prediction model based on machine learning is trained from historical surveillance data to represent the collective behavior of a set of flight trajectories, from which the data-driven prediction can be obtained as the expected future behavior of an incoming flight. A physics-based estimation algorithm called Residual-Mean Interacting Multiple Models (RM-IMM) then incorporates the machine learning prediction as a pseudo-measurement to account for the current motion of the aircraft. The proposed framework is tested, with real air traffic surveillance data, by predicting the future state information of the flights for real-time air traffic control applications. The results show that the proposed framework produces a greatly improved prediction accuracy compared to the two existing machine learning-based algorithms. |
| first_indexed | 2024-12-14T08:03:21Z |
| format | Article |
| id | doaj.art-655ed8e18f544876a4bc4fe38b9b90ff |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T08:03:21Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-655ed8e18f544876a4bc4fe38b9b90ff2022-12-21T23:10:17ZengIEEEIEEE Access2169-35362021-01-01915118615119710.1109/ACCESS.2021.31261179605684Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal AirspaceHong-Cheol Choi0https://orcid.org/0000-0002-7604-8639Chuhao Deng1https://orcid.org/0000-0001-6950-5558Inseok Hwang2https://orcid.org/0000-0001-7847-9865School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, USASchool of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, USASchool of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, USAFor air traffic management, trajectory prediction plays an important role as the predicted trajectory information is used in crucial tasks for the safety and efficiency of air traffic operations, such as conflict detection and resolution, scheduling, and sequencing. In this paper, we propose a framework for trajectory prediction in terminal airspace by combining a machine learning-based method and a physics-based estimation method. A trajectory prediction model based on machine learning is trained from historical surveillance data to represent the collective behavior of a set of flight trajectories, from which the data-driven prediction can be obtained as the expected future behavior of an incoming flight. A physics-based estimation algorithm called Residual-Mean Interacting Multiple Models (RM-IMM) then incorporates the machine learning prediction as a pseudo-measurement to account for the current motion of the aircraft. The proposed framework is tested, with real air traffic surveillance data, by predicting the future state information of the flights for real-time air traffic control applications. The results show that the proposed framework produces a greatly improved prediction accuracy compared to the two existing machine learning-based algorithms.https://ieeexplore.ieee.org/document/9605684/Aircraft trajectory predictionterminal airspacemachine learningGaussian mixture modellong short-term memory networkresidual-mean interacting multiple models |
| spellingShingle | Hong-Cheol Choi Chuhao Deng Inseok Hwang Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace IEEE Access Aircraft trajectory prediction terminal airspace machine learning Gaussian mixture model long short-term memory network residual-mean interacting multiple models |
| title | Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace |
| title_full | Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace |
| title_fullStr | Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace |
| title_full_unstemmed | Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace |
| title_short | Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace |
| title_sort | hybrid machine learning and estimation based flight trajectory prediction in terminal airspace |
| topic | Aircraft trajectory prediction terminal airspace machine learning Gaussian mixture model long short-term memory network residual-mean interacting multiple models |
| url | https://ieeexplore.ieee.org/document/9605684/ |
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