宇航学报 ›› 2022, Vol. 43 ›› Issue (4): 413-422.doi: 10.3873/j.issn.1000-1328.2022.04.003

• 飞行器设计与力学 • 上一篇    下一篇

一种高超声速滑翔飞行器轨迹智能预测方法

张君彪,熊家军,兰旭辉,席秋实,夏亮,张凯   

  1. 1. 空军预警学院预警情报系,武汉 430019;2. 78090部队,成都 610000
  • 收稿日期:2021-06-28 修回日期:2021-08-01 出版日期:2022-04-15 发布日期:2022-04-15
  • 基金资助:
    军队重点科研课题(KJ20191A020148);军事类研究生资助课题(JY2019B138,JY2018A039

An Intelligent Prediction Method of Hypersonic Glide Vehicle Trajectory

ZHANG Jun biao, XIONG Jia jun, LAN Xu hui, XI Qiu shi, XIA Liang, ZHANG Kai   

  1. 1. Early Warning Intelligence Department, Air Force Early Warning Academy, Wuhan 430019, China;2. Unit 78090, Chengdu 610000, China
  • Received:2021-06-28 Revised:2021-08-01 Online:2022-04-15 Published:2022-04-15

摘要: 针对高超声速滑翔飞行器(Hypersonic glide vehicle, HGV)机动性强、轨迹预测困难的问题,选取气动加速度作为预测参数,提出了一种基于集合经验模态分解和注意力长短时记忆网络的HGV轨迹智能预测方法。首先,以HGV六自由度运动方程为基础,分析了其机动特性和气动力变化规律,建立了动力学跟踪模型,对气动加速度进行实时估计;其次,利用集合经验模态分解对估计的气动加速度进行分解和重构,减弱噪声影响,避免对预测模型的干扰;最后,利用去噪后的气动加速度数据对注意力长短时记忆网络进行训练,进而预测未来气动加速度数据并重构HGV未来轨迹,实现轨迹的在线预测。实验仿真表明,该方法能有效预测HGV机动轨迹,预测精度高、稳定性好。

关键词: 高超声速滑翔飞行器, 轨迹预测, 去噪, 集合经验模态分解, 长短时记忆网络

Abstract: In order to solve the problem of high maneuverability and difficult trajectory prediction of hypersonic glide vehicle (HGV), an intelligent trajectory prediction method of HGV based on ensemble empirical mode decomposition and attention long short term memory network is proposed by selecting the aerodynamic acceleration as the prediction parameter. Firstly, the maneuvering characteristics and the aerodynamic variation law of HGV are analyzed based on the six degree of freedom motion equation. The dynamic tracking model is established to estimate the aerodynamic acceleration in real time. Secondly, the estimated aerodynamic acceleration is decomposed and reconstructed by using ensemble empirical mode decomposition to weaken the influence of noise and avoid interference to the prediction model. Finally, the denoised aerodynamic acceleration data used to train the attention long short term memory network. Then the future aerodynamic acceleration data predicted and the future trajectory of HGV is reconstructed to achieve online trajectory prediction. The simulation results show that the method can effectively predict the maneuver trajectory of HGV with high prediction accuracy and good stability.

Key words: Hypersonic glide vehicle, Trajectory prediction, Denoising, Ensemble empirical mode decomposition, Long short term memory network

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