TY - GEN
T1 - WGAN-GP and LSTM based Prediction Model for Aircraft 4- D Traj ectory
AU - Zhang, Lei
AU - Chen, Huiping
AU - Jia, Peiyan
AU - Tian, Zhihong
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The rapid growth of air traffic flow has brought the airspace capacity close to saturation and, at the same time, has resulted in great stress for air traffic controllers. The 4- D trajectory-based operation system is an important solution to problems in the current civil aviation field. The system mainly relies on accurate 4-D trajectory prediction technology to share trajectory information among air traffic control, airlines, and aircraft to achieve coordinated decision-making between flight and control. However, due to the complexity of trajectory data processing, the current 4-D trajectory prediction technology cannot meet actual needs. Therefore, a data generation and prediction network model (DGPNM) is proposed. It integrates the Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) and long-short-term memory (LSTM) neu-ral networks. With its outstanding performance, the LSTM neural network is utilized in both the generation module and the prediction module. The proposed model generates plenty of sample data to enlarge the train set, so overfitting could be reduced in the process of LSTM training. Experimental results prove that compared with other classical methods, the altitude prediction accuracy in the proposed model far exceeds that in current research results, which improves the prediction accuracy of the 4- D trajectory.
AB - The rapid growth of air traffic flow has brought the airspace capacity close to saturation and, at the same time, has resulted in great stress for air traffic controllers. The 4- D trajectory-based operation system is an important solution to problems in the current civil aviation field. The system mainly relies on accurate 4-D trajectory prediction technology to share trajectory information among air traffic control, airlines, and aircraft to achieve coordinated decision-making between flight and control. However, due to the complexity of trajectory data processing, the current 4-D trajectory prediction technology cannot meet actual needs. Therefore, a data generation and prediction network model (DGPNM) is proposed. It integrates the Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) and long-short-term memory (LSTM) neu-ral networks. With its outstanding performance, the LSTM neural network is utilized in both the generation module and the prediction module. The proposed model generates plenty of sample data to enlarge the train set, so overfitting could be reduced in the process of LSTM training. Experimental results prove that compared with other classical methods, the altitude prediction accuracy in the proposed model far exceeds that in current research results, which improves the prediction accuracy of the 4- D trajectory.
KW - LSTM neural network
KW - WGAN-GP
KW - traffic flow
KW - trajectory prediction
UR - http://www.scopus.com/inward/record.url?scp=85135344962&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135344962&partnerID=8YFLogxK
U2 - 10.1109/IWCMC55113.2022.9824928
DO - 10.1109/IWCMC55113.2022.9824928
M3 - Conference contribution
AN - SCOPUS:85135344962
T3 - 2022 International Wireless Communications and Mobile Computing, IWCMC 2022
SP - 937
EP - 942
BT - 2022 International Wireless Communications and Mobile Computing, IWCMC 2022
T2 - 18th IEEE International Wireless Communications and Mobile Computing, IWCMC 2022
Y2 - 30 May 2022 through 3 June 2022
ER -