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基于AVSIMM算法的高超声速再入滑翔目标跟踪

肖楚晗 李炯 雷虎民 王华吉

肖楚晗, 李炯, 雷虎民, 等 . 基于AVSIMM算法的高超声速再入滑翔目标跟踪[J]. 北京航空航天大学学报, 2019, 45(2): 413-421. doi: 10.13700/j.bh.1001-5965.2018.0314
引用本文: 肖楚晗, 李炯, 雷虎民, 等 . 基于AVSIMM算法的高超声速再入滑翔目标跟踪[J]. 北京航空航天大学学报, 2019, 45(2): 413-421. doi: 10.13700/j.bh.1001-5965.2018.0314
XIAO Chuhan, LI Jiong, LEI Humin, et al. Hypersonic reentry gliding target tracking based on AVSIMM algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2): 413-421. doi: 10.13700/j.bh.1001-5965.2018.0314(in Chinese)
Citation: XIAO Chuhan, LI Jiong, LEI Humin, et al. Hypersonic reentry gliding target tracking based on AVSIMM algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2): 413-421. doi: 10.13700/j.bh.1001-5965.2018.0314(in Chinese)

基于AVSIMM算法的高超声速再入滑翔目标跟踪

doi: 10.13700/j.bh.1001-5965.2018.0314
基金项目: 

国家自然科学基金 61573374

国家自然科学基金 61503408

详细信息
    作者简介:

    肖楚晗  女, 硕士研究生。主要研究方向:临近空间高超声速目标跟踪

    李炯  男, 博士, 副教授。主要研究方向:导航制导与控制

    通讯作者:

    李炯, E-mail: graceful1001@126.com

  • 中图分类号: TJ765.1

Hypersonic reentry gliding target tracking based on AVSIMM algorithm

Funds: 

National Natural Science Foundation of China 61573374

National Natural Science Foundation of China 61503408

More Information
  • 摘要:

    针对跟踪高超声速目标的交互式多模型(IMM)算法中存在模型数量过多,模型之间竞争导致滤波精度降低的问题,在自适应网格交互式多模型(AGIMM)算法的基础上,提出了一种自适应变结构交互式多模型(AVSIMM)算法跟踪高超声速再入滑翔目标。根据高超声速无动力再入滑翔目标当前机动状态的角速度参数,在自适应调整当前时刻模型集中参数的同时,针对AGIMM算法运动学模型的单一性,设计了具有多种跟踪滤波运动学模型的AVSIMM算法,通过模型集参数与算法结构的双重自适应调整实现了对目标高精度的跟踪。仿真结果表明,与AGIMM算法相比,所设计的AVSIMM算法不仅对结构和参数都具有更强的自适应性,同时提高了高超目标的跟踪精度和跟踪效率。

     

  • 图 1  AHW目标与基站相对位置示意图

    Figure 1.  Schematic diagram of relative position of AHW target to base station

    图 2  目标运动真实轨迹

    Figure 2.  True trajectory of target moving

    图 3  目标真实轨迹与4种算法估计轨迹

    Figure 3.  True trajectory of target and estimation trajectories in four algorithms

    图 4  目标跟踪位置误差

    Figure 4.  Position error of target tracking

    图 5  目标速度误差

    Figure 5.  Velocity error of target tracking

    图 6  位置均方根误差

    Figure 6.  Root mean square error of position

    图 7  速度均方根误差

    Figure 7.  Root mean square error of velocity

    表  1  位置、速度估计误差绝对值之和

    Table  1.   Sum of absolute value of position and velocity estimation error

    算法 x方向位置误差/
    km
    y方向位置误差/
    km
    x方向速度误差/(km·s-1) y方向速度误差/(km·s-1)
    AVSIMMⅠ 14.97 150.44 8.11 27.02
    AVSIMMⅡ 20.27 169.79 9.02 34.19
    AGIMM 35.05 222.14 34.59 73.63
    IMM 45.83 306.13 28.30 185.63
    下载: 导出CSV

    表  2  位置、速度均方根误差之和及仿真时间

    Table  2.   Sum of root mean square error of position and velocity and simulation time

    算法 位置RMSE/km 速度RMSE/
    (km·s-1)
    单次仿真时间/s
    AVSIMMⅠ 188.0 34.50 1.47
    AVSIMMⅡ 213.70 44.15 1.45
    AGIMM 302.79 132.30 1.44
    IMM 3 896.73 234.55 4.32
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-05-30
  • 录用日期:  2018-08-10
  • 刊出日期:  2019-02-20

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