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Proceedings Paper

Short-term 4D trajectory prediction based on LSTM neural network
Author(s): Ping Han; Jucai Yue; Cheng Fang; Qingyan Shi; Jun Yang
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Paper Abstract

A novel short-term four-dimensional (4D) trajectory prediction model based on deep learning is proposed in this paper. The model is based on LSTM (Long Short-Term Memory) neural network. It consists of input layer, hidden layer and output layer. Original trajectory data is first preprocessed in order to form supervised learning sequences which are used as input of the model. LSTM cell is used in hidden layer, information flow from each LSTM unit to next moment includes the cell state and the hidden state, which can be used to implicitly model the motion state of the aircraft trajectory. Four-dimensional information of the predicted trajectory is obtained from the output of the model. Experimental results with real flight data show that the proposed method is more effective in improving the prediction accuracy and has better robustness to data sources than the existing aircraft performance models.

Paper Details

Date Published: 31 January 2020
PDF: 8 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270M (31 January 2020); doi: 10.1117/12.2550425
Show Author Affiliations
Ping Han, Civil Aviation Univ. of China (China)
Jucai Yue, Civil Aviation Univ. of China (China)
Cheng Fang, Civil Aviation Univ. of China (China)
Qingyan Shi, Civil Aviation Univ. of China (China)
Jun Yang, Civil Aviation Univ. of China (China)

Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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