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面向机动目标的分布式弹群在线协同定位方法

傅晋博 张栋 赵军民 王庭晖

傅晋博,张栋,赵军民,等. 面向机动目标的分布式弹群在线协同定位方法[J]. 北京航空航天大学学报,2024,50(3):1027-1036 doi: 10.13700/j.bh.1001-5965.2022.0361
引用本文: 傅晋博,张栋,赵军民,等. 面向机动目标的分布式弹群在线协同定位方法[J]. 北京航空航天大学学报,2024,50(3):1027-1036 doi: 10.13700/j.bh.1001-5965.2022.0361
FU J B,ZHANG D,ZHAO J M,et al. On-line co-location method of distributed missile swarms for maneuvering targets[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(3):1027-1036 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0361
Citation: FU J B,ZHANG D,ZHAO J M,et al. On-line co-location method of distributed missile swarms for maneuvering targets[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(3):1027-1036 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0361

面向机动目标的分布式弹群在线协同定位方法

doi: 10.13700/j.bh.1001-5965.2022.0361
基金项目: 国家自然科学基金(61933010,61903301)
详细信息
    通讯作者:

    E-mail:zhangdong@nwpu.edu.cn

  • 中图分类号: V 448

On-line co-location method of distributed missile swarms for maneuvering targets

Funds: National Natural Science Foundation of China (61933010,61903301)
More Information
  • 摘要:

    机动目标的高精度协同定位是协同打击的关键,通过分布式弹群来实现协同定位是目前研究的热点。提出了一种分布式弹群在线协同定位的策略,解决弹群通信受限条件下协同定位的实时性问题;针对目标状态估计中模型非线性、噪声非高斯分布等特点,提出了容积卡尔曼粒子滤波算法;设计了自适应转弯速率的匀速转弯模型,并将现有的二维匀速转弯模型扩展至三维,解决了现有匀速转弯模型先验转弯速率固定,导致定位精度不高的问题;设计了自适应模型转移概率的交互多模型方法,实时修正马尔可夫转移概率,解决了单模型滤波定位精度不高的问题。通过仿真验证了所提策略和方法的有效性和准确性。

     

  • 图 1  双弹协同定位示意图

    Figure 1.  Schematic diagram of double bomb positioning

    图 2  基于IMM的目标定位算法结构

    Figure 2.  Structure of target localization algorithm based on IMM

    图 3  分布式协同定位框架

    Figure 3.  Distributed positioning framework

    图 4  非全连通网络弹群信息流向示意图

    Figure 4.  Schematic diagram of information flow of non-fully connected network

    图 5  三维CT模型方向向量示意图

    Figure 5.  Schematic diagram of direction vector of 3D CT model

    图 6  匀速转弯示意图

    Figure 6.  Schematic diagram of CT

    图 7  容积卡尔曼粒子滤波流程

    Figure 7.  Flow chart of Cubature Kalman filter

    图 8  目标协同定位仿真场景

    Figure 8.  Target simulation scene diagram

    图 9  弹目运动轨迹示意图

    Figure 9.  Schematic diagram of ballistic trajectory

    图 10  滤波算法定位结果对比

    Figure 10.  Comparison of positioning results of filtering algorithms

    图 11  滤波算法算法精度对比

    Figure 11.  Algorithm accuracy comparison of filtering algorithm

    图 12  改进IMM算法轨迹误差对比

    Figure 12.  Comparison of trajectory errors of improved IMM algorithm

    图 13  改进IMM算法定位效果局部比较

    Figure 13.  Local comparison of positioning effect of improved IMM algorithm

    图 14  改进IMM算法速度误差对比

    Figure 14.  Comparison of speed error of improved IMM algorithm

    表  1  弹群协同定位仿真参数

    Table  1.   Missile swarm simulation parameters

    仿真参数 数组/范围
    目标初始状态$ {{\boldsymbol{X}}_T} $ $ {\left[ {\begin{array}{*{20}{c}} 0&0&0&0&0&0&0&0&0 \end{array}} \right]^{\mathrm{T}}} $
    弹群节点数 5
    导弹初始位置范围/km $ - 20 < x_m^i,y_m^i,{\textit{z}}_m^i < 20 $
    采样步长/s 1
    下载: 导出CSV

    表  2  仿真结果

    Table  2.   Simulation result table

    横向对比约束算法平均RMSE/m平均运行时间/ms
    相同运行时间PF902.866.13
    CPF310.815.43
    CKF387.520.32
    相同定位精度PF412.6548.70
    CPF394.563.96
    CKF387.520.32
    下载: 导出CSV

    表  3  IMM算法定位性能对比

    Table  3.   Comparison of positioning performance of IMM algorithms

    算法平均RMSE/m平均运行时间/s
    标准IMM133.0521.78
    改进IMM99.7822.05
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-05-16
  • 录用日期:  2022-07-08
  • 网络出版日期:  2022-07-22
  • 整期出版日期:  2024-03-27

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