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论文:2022,Vol:40,Issue(3):628-635 |
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引用本文： |
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吴慈航, 闫建国, 程龙, 王嘉伟, 郭一鸣, 邢小军. 基于多头卷积长短期记忆网络的锥套轨迹预测[J]. 西北工业大学学报 |
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WU Cihang, YAN Jianguo, CHENG Long, WANG Jiawei, GUO Yiming, XING Xiaojun. Predicting trajectory of drogue based on multi-head convolutional long-short-term memory network[J]. Northwestern polytechnical university |
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| 基于多头卷积长短期记忆网络的锥套轨迹预测 |
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| 吴慈航1, 闫建国1, 程龙2, 王嘉伟3, 郭一鸣1, 邢小军1 |
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1. 西北工业大学 自动化学院, 陕西 西安 710072;
2. 中央军委装备发展部, 北京 100000;
3. 中国航空工业集团公司 洛阳电光设备研究所, 河南 洛阳 471000 |
| 摘要: |
| 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。 |
| 关键词:
空中加油
LSTM
多头卷积网络
残差网络
轨迹预测
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| Predicting trajectory of drogue based on multi-head convolutional long-short-term memory network |
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| WU Cihang1, YAN Jianguo1, CHENG Long2, WANG Jiawei3, GUO Yiming1, XING Xiaojun1 |
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1. 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 |
| Abstract: |
| 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. |
| Key words:
aerial refueling
long-short-term memory
multi-head convolutional network
residual network
trajectory prediction
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| 收稿日期: 2021-08-09
修回日期:
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| DOI: 10.1051/jnwpu/20224030628 |
| 基金项目: 陕西省自然科学基础研究计划(2020JM-123)资助 |
| 通讯作者: 闫建国(1956—),西北工业大学教授,主要从事飞行控制研究。e-mail:yjg0311@nwpu.edu.cn
Email：yjg0311@nwpu.edu.cn |
| 作者简介: 吴慈航(1994—),西北工业大学博士研究生,主要从事自主空中加油研究。
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