TY - GEN
T1 - A Target Trajectory Prediction Method in Air Combat Based on Wavelet-Attention-GRU Under the Frenet Frame
AU - Zhang, An
AU - Mao, Zeming
AU - Xu, Haiyu
AU - Fan, Qiucen
AU - Bi, Wenhao
AU - Yan, Yuwen
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The target trajectory prediction method can assist pilots in situation awareness and provide support for decision-making to improve the capacity to gain the advantage during high dynamic within-visual-range air combat. Aiming at the problem of traditional methods based on the Cartesian frame, such as the low utilization of training data and the weak generalization, and the low accuracy of the existing time series prediction models, a target trajectory prediction method in air combat based on Wavelet-Attention-GRU under the Frenet frame is proposed. In this method, the air combat features are described via the spatial trajectory curve based on the Frenet frame; the target trajectory prediction model is established by combining the GRU network with the improved multi-head self-attention mechanism by adding wavelet transform. Finally, the one-to-one within-visual-range air combat dataset obtained via the high-fidelity air combat simulator is applied to train and test the trajectory prediction model.
AB - The target trajectory prediction method can assist pilots in situation awareness and provide support for decision-making to improve the capacity to gain the advantage during high dynamic within-visual-range air combat. Aiming at the problem of traditional methods based on the Cartesian frame, such as the low utilization of training data and the weak generalization, and the low accuracy of the existing time series prediction models, a target trajectory prediction method in air combat based on Wavelet-Attention-GRU under the Frenet frame is proposed. In this method, the air combat features are described via the spatial trajectory curve based on the Frenet frame; the target trajectory prediction model is established by combining the GRU network with the improved multi-head self-attention mechanism by adding wavelet transform. Finally, the one-to-one within-visual-range air combat dataset obtained via the high-fidelity air combat simulator is applied to train and test the trajectory prediction model.
UR - http://www.scopus.com/inward/record.url?scp=85217875640&partnerID=8YFLogxK
U2 - 10.1109/SMC54092.2024.10831508
DO - 10.1109/SMC54092.2024.10831508
M3 - 会议稿件
AN - SCOPUS:85217875640
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 509
EP - 514
BT - 2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024
Y2 - 6 October 2024 through 10 October 2024
ER -