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Attention-LSTM based prediction model for aircraft 4-D trajectory

Aviation activities are constantly increasing as a result of the growth of the global economic system. How to increase airspace capacity within the limited airspace resources while ensuring smooth and safe aircraft operations is a challenge for civil aviation today. Air traffic safety is supported b...

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Autores principales: Jia, Peiyan, Chen, Huiping, Zhang, Lei, Han, Daojun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9478158/
https://www.ncbi.nlm.nih.gov/pubmed/36109612
http://dx.doi.org/10.1038/s41598-022-19794-1
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author Jia, Peiyan
Chen, Huiping
Zhang, Lei
Han, Daojun
author_facet Jia, Peiyan
Chen, Huiping
Zhang, Lei
Han, Daojun
author_sort Jia, Peiyan
collection PubMed
description Aviation activities are constantly increasing as a result of the growth of the global economic system. How to increase airspace capacity within the limited airspace resources while ensuring smooth and safe aircraft operations is a challenge for civil aviation today. Air traffic safety is supported by accurate trajectory prediction. The way-points are relatively sparse, and there are many uncertain factors in the flight, which greatly increases the difficulty of trajectory prediction. So, it is vital to enhance trajectory prediction accuracy. An attention-LSTM trajectory prediction model is proposed in this paper, which is split into two parts. The time-series features of the flight trajectory are extracted in the initial stage using the long-short-term memory neural network (LSTM). In the second part, the attention mechanism is employed to process the extracted sequence features. The impact of secondary elements is reduced while the influence of primary ones is increased according to the attention mechanism. We used the advanced models in trajectory prediction as the comparison models, such as LSTM, support vector machine (SVM), back propagation (BP) neural network, Hidden Markov Model (HMM), and convolutional long-term memory neural network (CNN-LSTM). The model we proposed is superior to the model above based on quantitative analysis and comparison.
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spelling pubmed-94781582022-09-17 Attention-LSTM based prediction model for aircraft 4-D trajectory Jia, Peiyan Chen, Huiping Zhang, Lei Han, Daojun Sci Rep Article Aviation activities are constantly increasing as a result of the growth of the global economic system. How to increase airspace capacity within the limited airspace resources while ensuring smooth and safe aircraft operations is a challenge for civil aviation today. Air traffic safety is supported by accurate trajectory prediction. The way-points are relatively sparse, and there are many uncertain factors in the flight, which greatly increases the difficulty of trajectory prediction. So, it is vital to enhance trajectory prediction accuracy. An attention-LSTM trajectory prediction model is proposed in this paper, which is split into two parts. The time-series features of the flight trajectory are extracted in the initial stage using the long-short-term memory neural network (LSTM). In the second part, the attention mechanism is employed to process the extracted sequence features. The impact of secondary elements is reduced while the influence of primary ones is increased according to the attention mechanism. We used the advanced models in trajectory prediction as the comparison models, such as LSTM, support vector machine (SVM), back propagation (BP) neural network, Hidden Markov Model (HMM), and convolutional long-term memory neural network (CNN-LSTM). The model we proposed is superior to the model above based on quantitative analysis and comparison. Nature Publishing Group UK 2022-09-15 /pmc/articles/PMC9478158/ /pubmed/36109612 http://dx.doi.org/10.1038/s41598-022-19794-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Jia, Peiyan
Chen, Huiping
Zhang, Lei
Han, Daojun
Attention-LSTM based prediction model for aircraft 4-D trajectory
title Attention-LSTM based prediction model for aircraft 4-D trajectory
title_full Attention-LSTM based prediction model for aircraft 4-D trajectory
title_fullStr Attention-LSTM based prediction model for aircraft 4-D trajectory
title_full_unstemmed Attention-LSTM based prediction model for aircraft 4-D trajectory
title_short Attention-LSTM based prediction model for aircraft 4-D trajectory
title_sort attention-lstm based prediction model for aircraft 4-d trajectory
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9478158/
https://www.ncbi.nlm.nih.gov/pubmed/36109612
http://dx.doi.org/10.1038/s41598-022-19794-1
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