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...

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Autores principales:	Hong-Cheol Choi, Chuhao Deng, Inseok Hwang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Aircraft trajectory prediction terminal airspace machine learning Gaussian mixture model long short-term memory network residual-mean interacting multiple models Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/655ed8e18f544876a4bc4fe38b9b90ff
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id	oai:doaj.org-article:655ed8e18f544876a4bc4fe38b9b90ff
record_format	dspace
spelling	oai:doaj.org-article:655ed8e18f544876a4bc4fe38b9b90ff2021-11-17T00:00:31ZHybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace2169-353610.1109/ACCESS.2021.3126117https://doaj.org/article/655ed8e18f544876a4bc4fe38b9b90ff2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9605684/https://doaj.org/toc/2169-3536For 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.Hong-Cheol ChoiChuhao DengInseok HwangIEEEarticleAircraft trajectory predictionterminal airspacemachine learningGaussian mixture modellong short-term memory networkresidual-mean interacting multiple modelsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151186-151197 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Aircraft trajectory prediction terminal airspace machine learning Gaussian mixture model long short-term memory network residual-mean interacting multiple models Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Aircraft trajectory prediction terminal airspace machine learning Gaussian mixture model long short-term memory network residual-mean interacting multiple models Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hong-Cheol Choi Chuhao Deng Inseok Hwang Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace
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.
format	article
author	Hong-Cheol Choi Chuhao Deng Inseok Hwang
author_facet	Hong-Cheol Choi Chuhao Deng Inseok Hwang
author_sort	Hong-Cheol Choi
title	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_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_sort	hybrid machine learning and estimation-based flight trajectory prediction in terminal airspace
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/655ed8e18f544876a4bc4fe38b9b90ff
work_keys_str_mv	AT hongcheolchoi hybridmachinelearningandestimationbasedflighttrajectorypredictioninterminalairspace AT chuhaodeng hybridmachinelearningandestimationbasedflighttrajectorypredictioninterminalairspace AT inseokhwang hybridmachinelearningandestimationbasedflighttrajectorypredictioninterminalairspace
_version_	1718426051351150592

Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace

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