Short-term 4D trajectory prediction based on LSTM neural network

Ping Han; Jucai Yue; Cheng Fang; Qingyan Shi; Jun Yang

doi:10.1117/12.2550425

31 January 2020 Short-term 4D trajectory prediction based on LSTM neural network

Ping Han, Jucai Yue, Cheng Fang, Qingyan Shi, Jun Yang

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 114270M (2020) https://doi.org/10.1117/12.2550425
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

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.

Citation Download Citation

Ping Han, Jucai Yue, Cheng Fang, Qingyan Shi, and Jun Yang "Short-term 4D trajectory prediction based on LSTM neural network", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270M (31 January 2020); https://doi.org/10.1117/12.2550425

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