Volume 40, Number 3, June 2022
|Page(s)||628 - 635|
|Published online||19 September 2022|
Predicting trajectory of drogue based on multi-head convolutional long-short-term memory network
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
2 Equipment Development Department of China's Central Military Commission (CMC), Beijing 100000, China
3 Luoyang Institute of Electron-Optical Equipment, AVIC, Luoyang 471000, China
Aerial refueling is an important technology of great military significance. It can effectively boost an aircraft's performance owing to the longer period of time and longer endurance of range an aircraft can maintain in the air. To solve the problem that it is hard for a receiver aircraft to track a drogue during its docking phase, a drogue trajectory prediction method based on the multi-head convolutional long-short-term memory network is proposed. First, the one-dimensional time sequence data of the drogue is extended to its high-dimensional space. Then its spatial features are extracted through the multi-head convolutional residual network and fused together. On this basis, a long-short-term memory network is adopted to reveal the underlying temporal correlations among the spatial features and predict the trajectory of the drogue. The simulation and experimental results show that the method presented in this paper has a higher prediction accuracy than the traditional prediction methods that use time sequence data.
空中加油是一项具有重要军事意义的技术, 可有效提升飞机的滞空时间和航程距离。针对空中加油对接过程中受油机难以追踪锥套运动的难题, 提出了一种基于多头卷积长短期记忆网络的锥套轨迹预测方法。基于相空间重构技术将一维锥套轨迹序列数据映射至高维空间中, 采用多头卷积残差网络提取序列数据中的空间特征, 并进行特征融合。基于此, 采用长短期记忆网络挖掘特征中的时序关联, 并进行有效预测。计算机仿真实验和地面半物理实验结果表明, 所提的方法较传统时间序列预测方法具有更高的预测精度, 体现出潜在的工程应用前景。
Key words: aerial refueling / long-short-term memory / multi-head convolutional network / residual network / trajectory prediction
关键字 : 空中加油 / LSTM / 多头卷积网络 / 残差网络 / 轨迹预测
© 2022 Journal of Northwestern Polytechnical University. All rights reserved.
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