计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (9): 273-282.doi: 10.11896/jsjkx.230700149

• 人工智能 • 上一篇    下一篇

基于分阶段自编码器与注意力机制的舰载机着舰航迹实时预测模型

李哲1, 刘奕阳1, 王可1,2,3, 杨杰1, 李亚飞1,2,3, 徐明亮1,2,3   

  1. 1 郑州大学计算机与人工智能学院 郑州 450001
    2 国家超级计算郑州中心 郑州 450001
    3 智能集群系统教育部工程研究中心 郑州 450001
  • 收稿日期:2023-07-20 修回日期:2023-11-23 出版日期:2024-09-15 发布日期:2024-09-10
  • 通讯作者: 王可(iekwang@zzu.edu.cn)
  • 作者简介:(zheli@gs.zzu.edu.cn)
  • 基金资助:
    国家自然科学基金重点项目(62036010);河南省自然科学基金(232300421235);国防科技工业海洋防务技术创新中心创新基金(JJ-2022-709-01);中国博士后科学基金(2020M682348);国家自然科学基金面上项目(61972362,62372416)

Real-time Prediction Model of Carrier Aircraft Landing Trajectory Based on Stagewise Autoencoders and Attention Mechanism

LI Zhe1, LIU Yiyang1, WANG Ke1,2,3, YANG Jie1, LI Yafei1,2,3, XU Mingliang1,2,3   

  1. 1 School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China
    2 National Supercomputing Center in Zhengzhou,Zhengzhou 450001,China
    3 Intelligent Swarm System Engineering Research Center of the Ministry of Education,Zhengzhou 450001,China
  • Received:2023-07-20 Revised:2023-11-23 Online:2024-09-15 Published:2024-09-10
  • About author:LI Zhe,born in 1995,Ph.D candidate,is a member of CCF(No.U4062G).Her main research interests include intelligent perception and learning and so on.
    WANG Ke,born in 1985,Ph.D,lectu-rer,master's supervisor,is a member of CCF(No.P5447M).His main research interests include machine learning,deep learning,representation learning and their applications.
  • Supported by:
    This work was supported by theKey Program of the National Natural Science Foundation of China(62036010),Natural Science Foundation of Henan Province,China(232300421235),Innovation Foundation of Ocean Defense Technology Innovation Center of National Defence of Science,Technology and Industry(JJ-2022-709-01),China Postdoctoral Science Foundation(2020M682348) and General Program of the National Natural Science Foundation of China(61972362,62372416).

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摘要： 航空母舰舰载机着舰过程中应沿相对固定的航迹下滑,以保证触舰点位于舰艉拦阻系统所在的区域,因此舰载机航迹是着舰信号官进行指挥决策的重要依据之一。舰载机航迹实时预测有助于着舰信号官判断着舰作业发展态势,及时形成正确的航迹纠偏引导指令。为此,提出一种基于分阶段自编码器与注意力机制的着舰航迹实时预测模型。第一阶段采用降噪自编码器对历史航迹数据进行特征提取;第二阶段基于长短期记忆网络构建时序自编码器,同时引入注意力机制对不同时刻的编码器输出分配不同的权重,自适应学习其对最终预测结果的影响强度。通过仿真实验将所提模型与6种基线模型进行对比,结果表明,所提模型的综合性能优于基线模型,能够满足着舰航迹实时准确预测的应用需求。

关键词: 舰载机着舰, 航迹预测, 长短期记忆网络, 自编码器, 注意力机制

Abstract: During the landing process of an aircraft carrier,the carrier aircraft should fly along a relatively fixed trajectory to ensure that the touch point is located in the area where the stern arresting system is located.Therefore,the carrier aircraft trajectory is one of the important basis for the landing signal officer(LSO) to make decisions.The real-time prediction of carrier aircraft trajectory is helpful for the LSO to judge the situation of aircraft carrier landing operation and then form correct guidance instructions in time.Therefore,this paper proposes a real-time prediction of carrier aircraft landing trajectory based on stagewise auto-encoders and attention mechanism.In the first stage,a denoising autoencoder is used to extract features from historical trajectory data;in the second stage,a timeseries autoencoder is constructed based on a long short-term memory(LSTM),and at the same time,the attention mechanism is introduced to assign different weights to the encoder output at different times,and adaptively learns its influence on the final prediction result.The proposed model is compared with six baseline models through simulation experiments,and the results show that the comprehensive performance of the proposed model is better than that of the baseline model,which can meet the application requirements of real-time and accurate prediction of the landing trajectory.

Key words: Carrier aircraft landing, Trajectory prediction, Long short-term memory, Autoencoder, Attention mechanism

中图分类号: 

  • TP391
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