多扩展目标跟踪优化中基于威胁规避的无人机路径规划策略

陈辉 魏凤旗 韩崇昭

陈辉, 魏凤旗, 韩崇昭. 多扩展目标跟踪优化中基于威胁规避的无人机路径规划策略[J]. 雷达学报, 待出版. doi: 10.12000/JR22116
引用本文: 陈辉, 魏凤旗, 韩崇昭. 多扩展目标跟踪优化中基于威胁规避的无人机路径规划策略[J]. 雷达学报, 待出版. doi: 10.12000/JR22116
CHEN Hui, WEI Fengqi, and HAN Chongzhao. UAV path planning strategy based on threat avoidance in multiple extended target tracking optimization[J]. Journal of Radars, in press. doi: 10.12000/JR22116
Citation: CHEN Hui, WEI Fengqi, and HAN Chongzhao. UAV path planning strategy based on threat avoidance in multiple extended target tracking optimization[J]. Journal of Radars, in press. doi: 10.12000/JR22116

多扩展目标跟踪优化中基于威胁规避的无人机路径规划策略

doi: 10.12000/JR22116
基金项目: 国家自然科学基金项目(62163023, 62173266, 62103318, 61873116),甘肃省教育厅产业支撑计划项目(2021CYZC-02)
详细信息
    作者简介:

    陈 辉,博士,教授,博士生导师,主要研究方向为雷达目标跟踪、数据融合与电子对抗等

    魏凤旗,硕士生,研究方向为数据融合与多目标跟踪技术

    韩崇昭,教授,主要研究方向为多源信息融合、随机控制与自适应控制、非线性频谱分析等

    通讯作者:

    陈辉 huich78@hotmail.com

  • 责任主编:易伟 Corresponding Editor: YI Wei
  • 中图分类号: TP274

UAV Path Planning Strategy Based on Threat Avoidance in Multiple Extended Target Tracking Optimization

Funds: The National Natural Science Foundation of China (62163023, 62173266, 62103318, 61873116), The Industrial Support Project of Education Department of Gansu Province (2021CYZC-02)
More Information
  • 摘要: 为了降低无人机执行侦察任务时被摧毁的概率,该文提出一种有效减少威胁的路径规划算法。首先利用高分辨率机载雷达对多扩展目标进行稳健的跟踪估计,然后根据三向决策规则对各目标按威胁进行分类,并利用模糊理想解相似性排序技术(TOPSIS)的方法计算目标威胁度,综合多任务决策联合优化(联合评估目标威胁度和目标跟踪质量)作为评价准则对无人机进行路径规划。仿真实验表明,模糊威胁度评估方法在多扩展目标跟踪环境下是有效的,所提无人机路径规划算法是合理的,在不损失目标跟踪精度的条件下有效降低了目标威胁度。

     

  • 图  1  目标威胁评估过程

    Figure  1.  Target threat assessment process

    图  2  路径规划的基本原理图

    Figure  2.  Basic schematic diagram of path planning

    图  3  目标状态图示

    Figure  3.  Target status diagram

    图  4  目标威胁度评估

    Figure  4.  Target threat assessment

    图  5  目标实际轨迹与UAV原始轨迹

    Figure  5.  Actual target trajectory and UAV original trajectory

    图  6  穿越敌占区的UAV轨迹

    Figure  6.  UAV track crossing enemy occupied area

    图  7  完全自保的UAV轨迹

    Figure  7.  Fully self insured UAV trajectory

    图  8  MC实验中穿越敌占区的UAV轨迹分布

    Figure  8.  Trajectory distribution of UAV crossing enemy occupied area in MC experiment

    图  9  MC实验中完全自保的UAV轨迹分布

    Figure  9.  Trajectory distribution of fully self protected UAV in MC experiment

    图  10  目标威胁度评估统计均值

    Figure  10.  Statistical mean value of target threat assessment

    图  11  多扩展目标跟踪效果图

    Figure  11.  Multi-extended target tracking rendering

    图  12  目标质心位置GOSPA距离统计

    Figure  12.  GOSPA distance statistics of target centroid position

    图  13  目标形状(椭圆长短轴)估计GOSPA距离统计

    Figure  13.  Target shape (major and minor axes of ellipse) estimation GOSPA distance statistics

    图  14  多目标势估计

    Figure  14.  Multi-objective cardinality estimation

    表  1  分类风险函数

    Table  1.   Classification risk function

    分类行为$ A({\text{P}}) $$ \neg A({\text{N}}) $
    $ {a_{\text{P}}} $$ {\lambda _{{\text{PP}}}} $$ {\lambda _{{\text{PN}}}} $
    $ {a_{\text{B}}} $$ {\lambda _{{\text{BP}}}} $$ {\lambda _{{\text{BN}}}} $
    $ {a_{\text{N}}} $$ {\lambda _{{\text{NP}}}} $$ {\lambda _{{\text{NN}}}} $
    下载: 导出CSV

    表  2  GIW-MBer预测过程

    Table  2.   GIW-MBer prediction process

     输入:${\boldsymbol{\zeta}} _{k - 1}^{\left( {i,j} \right)}$。
     预测第j个GIW分量的参数:
        ${\boldsymbol{m}}_{k|k - 1}^{\left( {i,j} \right)} = {\boldsymbol{F}}_{k|k - 1}^i{\boldsymbol{m}}_{k - 1}^{\left( {i,j} \right)}$
        ${\boldsymbol{P}}_{k|k - 1}^{\left( {i,j} \right)} = {\boldsymbol{F}}_{k|k - 1}^i{\boldsymbol{P}}_{k - 1}^{\left( {i,j} \right)}{\left( {{\boldsymbol{F}}_{k|k - 1}^i} \right)^{\rm T} } + {{\boldsymbol{Q}}_k}$
        $v_{k|k - 1}^{\left( {i,j} \right)} = {{\rm{e}}^{ - \frac{ { {T_s} } }{\tau } } }v_{k - 1}^{\left( {i,j} \right)}$,其中$ \tau $为时间衰减常数
        ${\boldsymbol{V} }_{k|k - 1}^{\left( {i,j} \right)} = \dfrac{ {v_{k|k - 1}^{\left( {i,j} \right)} - d - 1} }{ {v_{k - 1}^{\left( {i,j} \right)} - d - 1} }{\boldsymbol{V}}_{k - 1}^{\left( {i,j} \right)}$
        ${\boldsymbol{X}}_{k|k - 1}^{\left( {i,j} \right)} = \dfrac{ {{\boldsymbol{V}}_{k|k - 1}^{\left( {i,j} \right)} } }{ {v_{k|k - 1}^{\left( {i,j} \right)} - 2d - 2} }$
     输出:${\boldsymbol{\zeta}} _{k|k - 1}^{\left( {i,j} \right)}$。
    下载: 导出CSV

    表  3  GIW-MBer更新过程

    Table  3.   GIW-MBer update process

     输入:${\boldsymbol{\zeta}} _{k|k - 1}^{\left( {i,j} \right)}$,量测集划分W
     更新第j个GIW分量的参数:
        $\bar {\boldsymbol{z} }_k^{\boldsymbol{W} } = \dfrac{1}{ {\left| {\boldsymbol{W} } \right|} }\displaystyle\sum\limits_{{\boldsymbol{z}}_k^{\left( i \right)} \in {\boldsymbol{W}}} { {\boldsymbol{z} }_k^{\left( i \right)} }$
        ${\boldsymbol{X} }_{k|k - 1}^{\left( {i,j} \right)} = \dfrac{ { {\boldsymbol{V} }_{k|k - 1}^{\left( {i,j} \right)} } }{ {v_{k|k - 1}^{\left( {i,j} \right)} - 2d - 2} }$
        ${\boldsymbol{S} }_{k|k - 1}^{\left( {i,j,W} \right)} = { {\boldsymbol{H} }_k}{\boldsymbol{P} }_{k|k - 1}^{\left( {i,j} \right)}{\boldsymbol{H} }_k^{\text{T} } + \dfrac{ { {\boldsymbol{X} }_{k|k - 1}^{\left( {i,j} \right)} } }{ {\left| {\boldsymbol{W} } \right|} }$
        ${\boldsymbol{K}}_{k|k - 1}^{\left( {i,j,{\boldsymbol{W}}} \right)} = {\boldsymbol{P}}_{k|k - 1}^{\left( {i,j} \right)}{\boldsymbol{H}}_k^{\text{T} }{\left( {{\boldsymbol{S}}_{k|k - 1}^{\left( {i,j,{\boldsymbol{W}}} \right)} } \right)^{ - 1} }$
        ${\boldsymbol{\varepsilon} } _{k|k - 1}^{\left( {i,j,{\boldsymbol{W} } } \right)} = \bar {\boldsymbol{z} }_k^{\boldsymbol{W} } - { {\boldsymbol{H} }_k}{\boldsymbol{m} }_{k|k - 1}^{\left( {i,j} \right)}$
        ${\boldsymbol{m}}_k^{\left( {i,j} \right)} = {\boldsymbol{m}}_{k|k - 1}^{\left( {i,j} \right)} + {\boldsymbol{K}}_{k|k - 1}^{\left( {i,j,{\boldsymbol{W}}} \right)}{\boldsymbol{\varepsilon}} _{k|k - 1}^{\left( {i,j,{\boldsymbol{W}}} \right)}$
        ${\boldsymbol{P} }_k^{\left( {i,j} \right)} = {\boldsymbol{P} }_{k|k - 1}^{\left( {i,j} \right)} - {\boldsymbol{K} }_{k|k - 1}^{\left( {i,j,{\boldsymbol{W} } } \right)}{\boldsymbol{S}}_{k|k - 1}^{\left( {i,j,{\boldsymbol{W} } } \right)}{\left( { {\boldsymbol{K} }_{k|k - 1}^{\left( {i,j,{\boldsymbol{W} } } \right)} } \right)^{\text{T} } }$
        ${\boldsymbol{Z} }_k^{\boldsymbol{W}} = \displaystyle\sum\limits_{ {\boldsymbol{z} }_k^{\left( i \right)} \in {\boldsymbol{W} } } {\left( { {\boldsymbol{z} }_k^{\left( i \right)} - \bar {\boldsymbol{z} }_k^{\boldsymbol{W}}} \right){ {\left( { {\boldsymbol{z} }_k^{\left( i \right)} - \bar {\boldsymbol{z} }_k^{\boldsymbol{W}}} \right)}^{\text{T} } } }$
        $\begin{aligned} {\boldsymbol{N} }_{k|k - 1}^{\left( {i,j,{\boldsymbol{W}}} \right)} =& {\left( { {\boldsymbol{X} }_{k|k - 1}^{\left( {i,j} \right)} } \right)^{\frac{1}{2} } }{\left( { {\boldsymbol{S} }_{k|k - 1}^{\left( {i,j,{\boldsymbol{W}}} \right)} } \right)^{ - \frac{1}{2} } }{\boldsymbol{\varepsilon} } _{k|k - 1}^{\left( {i,j,{\boldsymbol{W} } } \right)} \\ & \times {\left( { {\boldsymbol{\varepsilon} } _{k|k - 1}^{\left( {i,j,{\boldsymbol{W} } } \right)} } \right)^{\text{T} } }{\left( { {\boldsymbol{S} }_{k|k - 1}^{\left( {i,j,{\boldsymbol{W} } } \right)} } \right)^{ - \frac{ {\text{T} } }{2} } }{\left( { {\boldsymbol{X} }_{k|k - 1}^{\left( {i,j} \right)} } \right)^{\frac{ {\text{T} } }{2} } } \end{aligned}$
        $v_k^{\left( {i,j,{\boldsymbol{W}}} \right)} = v_{k|k - 1}^{\left( {i,j,{\boldsymbol{W}}} \right)} + \left| {\boldsymbol{W} } \right|$
        ${\boldsymbol{V} }_k^{\left( {i,j,{\boldsymbol{W} } } \right)} = {\boldsymbol{V} }_{k|k - 1}^{\left( {i,j,{\boldsymbol{W} } } \right)} + {\boldsymbol{N} }_{k|k - 1}^{\left( {i,j,{\boldsymbol{W}}} \right)} + {\boldsymbol{Z} }_k^{\boldsymbol{W} }$
        ${\boldsymbol{X} }_k^{\left( {i,j,{\boldsymbol{W} } } \right)} = \dfrac{ { {\boldsymbol{V} }_k^{\left( {i,j,{\boldsymbol{W} } } \right)} } }{ {v_k^{\left( {i,j,{\boldsymbol{W} } } \right)} - 2d - 2} }$
     输出:${\boldsymbol{\zeta}} _k^{\left( {i,j} \right)}$。
    下载: 导出CSV

    表  4  基于威胁规避的UAV路径规划算法

    Table  4.   UAV path planning algorithm for threat avoidance

     输入:$ k - 1 $时刻多扩展目标多特征信息${{\boldsymbol{\zeta}} _{k - 1} }$与UAV坐标
       ${{\boldsymbol{x}}_{s,k - 1} }$,其中${{\boldsymbol{\zeta}} _{k - 1} } = \left\{ { { {\boldsymbol{m} }_{k - 1} },{ {\boldsymbol{P} }_{k - 1} },{v_{k - 1} },{ {\boldsymbol{V} }_{k - 1} } } \right\}$。
     步骤1 多扩展目标跟踪的预测过程,得到$ {f_{k|k - 1}}\left( { \cdot | \cdot } \right) $。
     步骤2 路径规划:
        ${\hat {\boldsymbol{\xi}} _{k|k - 1} } = {\text{Sfun} }\left\{ { {f_{k|k - 1} }\left( { \cdot | \cdot } \right)} \right\}$,
        确定所有可能的路径规划方案${{\boldsymbol{C}}_k}$。
        ${\text{for all } }c \in {{\boldsymbol{C}}_k}{\text{ do} }$
          生成PIMS:${{\boldsymbol{Z}}_k}\left( u \right)$,
          量测集划分:${\boldsymbol{\rho}} \angle {{\boldsymbol{Z}}_k}\left( u \right)$,
          计算伪更新后验密度$ {f_{k,c}}\left( { \cdot | \cdot } \right) $,
          提取状态的统计平均:${\hat {\boldsymbol{\xi}} _{k,c} } \leftarrow {\text{Sfun} }\left\{ { {f_{k,c} }\left( { \cdot | \cdot } \right)} \right\}$,
          计算$\mathcal{D}\left( { {{\boldsymbol{\xi}} _{k,c} },{ {\bar {\boldsymbol{\xi}} }_{k,c} } } \right)$和$ \mathcal{V}\left( c \right) $。
        $ {\text{end for}} $
        求解控制方案:${\hat c_k} = \mathop {\arg \min }\limits_{c \in {{\boldsymbol{C}}_k} } \{ {w_\mathcal{V} }\mathcal{V}\left( c \right)$
        $+ {w_\mathcal{D} }\mathcal{D}({{\boldsymbol{\xi}} _{k,c} },{\bar {\boldsymbol{\xi}} _{k,c} })\}$。
     步骤3 多扩展目标跟踪的更新过程,得到$ {f_{k|k}}\left( { \cdot | \cdot } \right) $。
     步骤4 提取多扩展目标状态信息${{\boldsymbol{\xi}} _k}$,计算目标势${N_k} = \left| { {{\boldsymbol{\xi}} _k} } \right|$。
     输出:k时刻UAV坐标${{\boldsymbol{x}}_{s,k} }$,目标势$ {N_k} $,多扩展目标状态集${{\boldsymbol{\xi}} _k}$。
    下载: 导出CSV

    表  5  硬件配置

    Table  5.   Hardware configuration

    参数数值
    CPU主频3.1 GHz
    最高睿频5.2 GHz
    内存类型DDR4 3200 MHz
    最大内存带宽76.8 GB/s
    下载: 导出CSV

    表  6  目标状态

    Table  6.   Target status

    目标编号位置(m; m)速度(m/s; m/s)运动方向(°)
    1[100; 100][–10; –10]0
    2[200; 200][–10; –10]0
    3[100; 100][–5; –5]0
    4[–100; 100][10; –10]0
    5[100; 100][5; 5]180
    6[100; 100][–10; 10]90
    下载: 导出CSV

    表  7  各运动体的初始状态

    Table  7.   Initial state of each moving object

    目标出生时刻
    (s)
    消亡时刻
    (s)
    初始状态
    (m; m; m/s; m/s)
    目标1140[–300; 100; 30; –10]
    目标21140[100; 200; –15; –30]
    目标31635[50; –600; –20; 30]
    目标42135[–600; –200; 10; 35]
    目标52630[200; 500; –50; 20]
    目标62630[–600; 600; 40; –30]
    UAV//[600; –800; –30; 40]
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-06-17
  • 修回日期:  2022-07-22
  • 网络出版日期:  2022-08-11

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