北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (11): 2094-2105.doi: 10.13700/j.bh.1001-5965.2020.0050

• 论文 • 上一篇    下一篇

基于SVM和EKF的高超声速滑翔飞行器轨迹预报

程云鹏1, 孙成志1, 闫晓东1,2   

  1. 1. 西北工业大学 航天学院, 西安 710072;
    2. 陕西省空天飞行器设计技术重点实验室, 西安 710072
  • 收稿日期:2020-02-25 发布日期:2020-12-01
  • 通讯作者: 闫晓东 E-mail:yan804@nwpu.edu.cn
  • 作者简介:程云鹏,男,博士研究生。主要研究方向:机动目标跟踪和预报;孙成志,男,硕士研究生。主要研究方向:航天器故障诊断和识别;闫晓东,男,博士,副教授,硕士生导师。主要研究方向:飞行器动力学与制导。

Trajectory prediction of hypersonic glide vehicle based on SVM and EKF

CHENG Yunpeng1, SUN Chengzhi1, YAN Xiaodong1,2   

  1. 1. School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China;
    2. Shaanxi Aerospace Flight Vehicle Design Key Laboratory, Xi'an 710072, China
  • Received:2020-02-25 Published:2020-12-01

摘要: 高超声速滑翔飞行器(HGV)拦截问题中,轨迹预报是成功拦截的重要基础。针对HGV机动能力强、轨迹多变的特点,提出了一种基于支持向量机(SVM)和扩展卡尔曼滤波(EKF)的轨迹预报方法。在HGV的滑翔段机动模式分析的基础上,将HGV的机动运动分解为纵向运动模式和侧向运动模式,进而对运动模式的特征参数予以标定,形成SVM的训练集。建立地基单雷达轨迹跟踪模型,采用EKF对HGV滑翔段轨迹进行稳定跟踪并实现对运动模式特征参数的估计。基于SVM,建立了HGV运动识别框架,实现了对HGV滑翔段轨迹的预报。对平衡滑翔和跳跃机动2种典型机动模式进行数学仿真验证,结果表明,所提方法可以提高对该类目标的轨迹预报精度。

关键词: 高超声速滑翔飞行器(HGV), 机动模式, 支持向量机(SVM), 运动识别, 轨迹估计和预报

Abstract: In the scenario of intercepting a Hypersonic Glide Vehicle (HGV), the trajectory prediction is a key issue for successful interception. Considering HGV's strong maneuverability and variable trajectory, in this paper, a novel trajectory prediction method is proposed based on Support Vector Machine (SVM) and Extended Kalman Filter (EKF). First, the investigation on the maneuvering mode is performed. The maneuver motion of the HGV is divided into longitudinal mode and lateral mode, which are labeled and formulated into the training set of SVMs. Second, the tracking model of the trajectory for single ground-based radar is established, and EKF is applied to track the glide trajectory of HGV. Finally, the recognition framework of HGV motion is established based on SVM, and the prediction of the subsequent trajectory is accomplished. The results show that the proposed method can improve the trajectory prediction accuracy of HGV.

Key words: Hypersonic Glide Vehicle (HGV), maneuver mode, Support Vector Machine (SVM), motion recognition, trajectory estimation and prediction

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