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多扩展目标跟踪中基于多特征优化的传感器控制方法

陈辉 魏凤旗 韩崇昭

陈辉, 魏凤旗, 韩崇昭. 多扩展目标跟踪中基于多特征优化的传感器控制方法[J]. 电子与信息学报, 2023, 45(1): 191-199. doi: 10.11999/JEIT211244
引用本文: 陈辉, 魏凤旗, 韩崇昭. 多扩展目标跟踪中基于多特征优化的传感器控制方法[J]. 电子与信息学报, 2023, 45(1): 191-199. doi: 10.11999/JEIT211244
CHEN Hui, WEI Fengqi, HAN Chongzhao. Sensor Control Based on Multiple Feature Optimization in Multiple Extended Targets Tracking[J]. Journal of Electronics & Information Technology, 2023, 45(1): 191-199. doi: 10.11999/JEIT211244
Citation: CHEN Hui, WEI Fengqi, HAN Chongzhao. Sensor Control Based on Multiple Feature Optimization in Multiple Extended Targets Tracking[J]. Journal of Electronics & Information Technology, 2023, 45(1): 191-199. doi: 10.11999/JEIT211244

多扩展目标跟踪中基于多特征优化的传感器控制方法

doi: 10.11999/JEIT211244
基金项目: 国家自然科学基金 (62163023, 61763029, 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:男,教授,博士生导师,主要研究方向为认知对抗、数据融合、最优控制等

    魏凤旗:男,硕士生,研究方向为数据融合与多目标跟踪技术

    韩崇昭:男,教授,博士生导师,主要研究方向为数据融合、电子对抗、雷达目标跟踪等

    通讯作者:

    陈辉 huich78@hotmail.com

  • 中图分类号: TN911.73; TP274

Sensor Control Based on Multiple Feature Optimization in Multiple Extended Targets Tracking

Funds: The National Natural Science Foundation of China (62163023, 61763029, 61873116), The Industrial Support Project of Education Department of Gansu Province (2021CYZC-02)
  • 摘要: 针对多扩展目标的优化跟踪问题,该文在有限集统计(FISST)理论框架下,提出一种能够综合优化多扩展目标跟踪性能的传感器控制方法。首先,该文给出加权广义最优子模式分配(WGOSPA)距离构造多扩展目标跟踪多特征估计在其统计平均周围的广义离差,进而研究提出多特征融合下的传感器控制最优决策方法,并利用序贯蒙特卡罗(SMC)技术研究传感器控制最优决策过程的数值求解方法,然后利用伽马高斯逆威沙特多伯努利(GGIW-MBer)滤波器实现所提出的传感器控制策略。最后通过仿真实验验证了所提算法的有效性。
  • 图  1  多扩展目标跟踪中的传感器控制基本原理图

    图  2  传感器动作空间示意图

    图  3  目标的实际轨迹

    图  4  方案2中的传感器运动轨迹

    图  5  MC实验中方案2传感器轨迹控制图

    图  6  目标质心位置估计GOSPA距离统计

    图  7  目标跟踪轨迹图

    图  8  椭圆长短轴GOSPA距离统计

    图  9  多扩展目标跟踪的势估计

    算法1 多扩展目标跟踪基于多特征优化的传感器控制算法
     输入:$ k - 1 $时刻多扩展目标多特征信息$ {\zeta _{k - 1}} $与传感器坐标
        ${x_{{\rm{s}},k - 1} }$,
     其中,${\zeta _{k - 1} } = \left\{ { {\alpha _{k - 1} },{\beta _{k - 1} },{{\boldsymbol{m}}_{k - 1} },{{\boldsymbol{P}}_{k - 1} },{{\boldsymbol{v}}_{k - 1} },{{\boldsymbol{V}}_{k - 1} } } \right\}$。
     (1) 多扩展目标跟踪的预测过程,得到$ {f_{k|k - 1}}\left( { \cdot | \cdot } \right) $。
     (2) 传感器控制
     $ {\hat \xi _{k|k - 1}} = {\text{Sef}}\left\{ {{f_{k|k - 1}}\left( { \cdot | \cdot } \right)} \right\} $,
     确定所有可能的控制方案${{\boldsymbol{U}}_k}$。
     ${\text{for all } }u \in {{\boldsymbol{U}}_k}{\text{ do} }$
      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,u}}\left( { \cdot | \cdot } \right) $,
      提取状态的统计平均:$ {\bar \xi _{k,u}} \leftarrow {\text{Sef}}\left\{ {{f_{k,u}}\left( { \cdot | \cdot } \right)} \right\} $,
      蒙特卡罗采样:$ \left\{ {{\xi _{k,l}}} \right\}_{l = 1}^L \leftarrow {\text{MC}}\left( {{f_{k,u}}\left( { \cdot | \cdot } \right),L} \right) $,
      $ \mathcal{V}\left( u \right) \leftarrow 0 $,
      $ {\text{for }}l = 1:L $
       $\mathcal{V}\left( u \right) \leftarrow \mathcal{V}\left( u \right) + \dfrac{1}{L}d_p^{\left( { {c_w},\alpha } \right)}\left( { {\xi _{k,l} },{ {\bar \xi }_{k,u} } } \right)$。
      $ {\text{end for}} $
     $ {\text{end for}} $
     $ {\hat u_k} \leftarrow \mathop {\arg \min }\limits_{u \in {U_k}} \mathcal{V}\left( u \right) $。
     (3) 多扩展目标跟踪的更新过程,得到$ {f_{k|k}}\left( { \cdot | \cdot } \right) $。
     (4) 提取状态信息$ {\xi _k} $,并计算目标势$ {N_k} = \left| {{\xi _k}} \right| $。
     输出:目标势$ {N_k} $,多扩展目标状态集$ {\xi _k} $,$ k $时刻传感器坐标
        ${x_{{\rm{s}},k} }$。
    下载: 导出CSV
    算法2 GGIW-MBer预测过程
     输入:$ \zeta _{k - 1}^{\left( {i,j} \right)} $。
     预测第$ j $个GGIW分量的参数
     ${\boldsymbol{m}}_{k|k - 1}^{\left( {i,j} \right)} = {{\boldsymbol{F}}_{k|k - 1} }{\boldsymbol{m}}_{k - 1}^{\left( {i,j} \right)}$
     ${\boldsymbol{P}}_{k|k - 1}^{\left( {i,j} \right)} = {{\boldsymbol{F}}_{k|k - 1} }{\boldsymbol{P}}_{k - 1}^{\left( {i,j} \right)}{\boldsymbol{F}}_{k|k - 1}^{\text{T} } + {{\boldsymbol{Q}}_k}$
     $v_{k|k - 1}^{\left( {i,j} \right)} = {{\rm{e}}^{ - \frac{ { {T_{\rm{s}}} } }{\tau } } }v_{k - 1}^{\left( {i,j} \right)}$
     $V_{k|k - 1}^{\left( {i,j} \right)} = \dfrac{ {v_{k|k - 1}^{\left( {i,j} \right)} - d - 1} }{ {v_{k - 1}^{\left( {i,j} \right)} - d - 1} }V_{k - 1}^{\left( {i,j} \right)}$
     $X_{k|k - 1}^{\left( {i,j} \right)} = \dfrac{ {V_{k|k - 1}^{\left( {i,j} \right)} } }{ {v_{k|k - 1}^{\left( {i,j} \right)} - 2d - 2} }$
     $\alpha _{k|k - 1}^{\left( {i,j} \right)} = \dfrac{ {\alpha _{k - 1}^{\left( {i,j} \right)} } }{ { {\eta _{k - 1} } } }$
     $\beta _{k|k - 1}^{\left( {i,j} \right)} = \dfrac{ {\beta _{k - 1}^{\left( {i,j} \right)} } }{ { {\eta _{k - 1} } } }$
     输出:$ \zeta _{k|k - 1}^{\left( {i,j} \right)} $。
    下载: 导出CSV
    算法3 GGIW-MBer更新过程
     输入:$ \zeta _{k|k - 1}^{\left( {i,j} \right)} $,量测集划分${\boldsymbol{W}}$。
     更新第$ j $个GGIW分量的参数
     $\bar z_k^W = \dfrac{1}{ {\left| {\boldsymbol{W} } \right|} }\displaystyle\sum\limits_{z_k^{\left( i \right)} \in W} { {\boldsymbol{z} }_k^{\left( i \right)} }$
     ${\boldsymbol{X}}_{k|k - 1}^{\left( {i,j} \right)} = \dfrac{ {{\boldsymbol{V}}_{k|k - 1}^{\left( {i,j} \right)} } }{ {v_{k|k - 1}^{\left( {i,j} \right)} - 2d - 2} }$
     ${\boldsymbol{S} }_{k|k - 1}^{\left( {i,j,W} \right)} = { {\boldsymbol{H} }_k}{\boldsymbol{P} }_{k|k - 1}^{\left( {i,j} \right)}{\boldsymbol{H} }_k^{\text{T} } + \dfrac{ { {\boldsymbol{X} }_{k|k - 1}^{\left( {i,j} \right)} } }{ {\left| {\boldsymbol{W} } \right|} }$
     ${\boldsymbol{K}}_{k|k - 1}^{\left( {i,j,W} \right)} = {\boldsymbol{P}}_{k|k - 1}^{\left( {i,j} \right)}{\boldsymbol{H}}_k^{\text{T} }{\left( {{\boldsymbol{S}}_{k|k - 1}^{\left( {i,j,W} \right)} } \right)^{ - 1} }$
     ${\boldsymbol{\varepsilon}} _{k|k - 1}^{\left( {i,j,W} \right)} = \bar {\boldsymbol{z}}_k^W - {{\boldsymbol{H}}_k}{\boldsymbol{m}}_{k|k - 1}^{\left( {i,j} \right)}$
     ${\boldsymbol{m}}_k^{\left( {i,j} \right)} = {\boldsymbol{m}}_{k|k - 1}^{\left( {i,j} \right)} + {\boldsymbol{K}}_{k|k - 1}^{\left( {i,j,W} \right)}{\boldsymbol{\varepsilon}} _{k|k - 1}^{\left( {i,j,W} \right)}$
     ${\boldsymbol{P}}_k^{\left( {i,j} \right)} = {\boldsymbol{P}}_{k|k - 1}^{\left( {i,j} \right)} - {\boldsymbol{K}}_{k|k - 1}^{\left( {i,j,W} \right)}{\boldsymbol{S}}_{k|k - 1}^{\left( {i,j,W} \right)}{\left( {{\boldsymbol{K}}_{k|k - 1}^{\left( {i,j,W} \right)} } \right)^{\text{T} } }$
     ${\boldsymbol{Z}}_k^W = \displaystyle\sum\limits_{z_k^{\left( i \right)} \in W} {\left( {{\boldsymbol{z}}_k^{\left( i \right)} - \bar {\boldsymbol{z}}_k^W} \right){ {\left( {{\boldsymbol{z}}_k^{\left( i \right)} - \bar {\boldsymbol{z}}_k^W} \right)}^{\text{T} } } }$
     $\begin{aligned} {\boldsymbol{N} }_{k|k - 1}^{\left( {i,j,W} \right)} =& {\left( { {\boldsymbol{X} }_{k|k - 1}^{\left( {i,j} \right)} } \right)^{\frac{1}{2} } }{\left( { {\boldsymbol{S} }_{k|k - 1}^{\left( {i,j,W} \right)} } \right)^{ - \frac{1}{2} } }{\boldsymbol{\varepsilon} } _{k|k - 1}^{\left( {i,j,W} \right)}{\text{ } } \times {\left( { {\boldsymbol{\varepsilon} } _{k|k - 1}^{\left( {i,j,W} \right)} } \right)^{\text{T} } }\\ & \cdot{\left(\left( { {\boldsymbol{S} }_{k|k - 1}^{\left( {i,j,W} \right)} } \right)^{ -\frac {1} {2} } \right)^{ {\rm{T} } } }\left({\left( { {\boldsymbol{X} }_{k|k - 1}^{\left( {i,j} \right)} } \right)^{\frac{ 1}{2} } }\right)^{\rm{T} }\end{aligned}$
     $v_k^{\left( {i,j,W} \right)} = v_{k|k - 1}^{\left( {i,j,W} \right)} + \left| {\boldsymbol{W}} \right|$
     ${\boldsymbol{V}}_k^{\left( {i,j,W} \right)} = {\boldsymbol{V}}_{k|k - 1}^{\left( {i,j,W} \right)} + {\boldsymbol{N}}_{k|k - 1}^{\left( {i,j,W} \right)} + {\boldsymbol{Z}}_k^W$
     ${\boldsymbol{X} }_k^{\left( {i,j,W} \right)} = \dfrac{ { {\boldsymbol{V} }_k^{\left( {i,j,W} \right)} } }{ {v_k^{\left( {i,j,W} \right)} - 2d - 2} }$
     $\alpha _k^{\left( {i,j,W} \right)} = \alpha _{k|k - 1}^{\left( {i,j,W} \right)} + \left| {\boldsymbol{W}} \right|$
     $ \beta _k^{\left( {i,j,W} \right)} = \beta _{k|k - 1}^{\left( {i,j,W} \right)} + 1 $
     输出:$ \zeta _k^{\left( {i,j} \right)} $。
    下载: 导出CSV

    表  1  多扩展目标初始参数

    目标出生时刻
    (s)
    消亡时刻
    (s)
    初始状态
    (m; m; m/s; m/s)
    1140[–800; 600; 40; –15]
    21140[–700; 0; 40; –10]
    32130[–100; 500; –35; –20]
    4110[200; 100; 10; 20]
    5120[–500; 100; –15; –15]
    63140[–100; 100; 20; –15]
    7615[500; 300; 10; 10]
    81625[–200; 300; –20; –60]
    92635[–200; –300; 40; –15]
    10130[300; –100; –20; –20]
    下载: 导出CSV

    表  2  目标质心估计的GOSPA距离统计均值(m)

    方案1方案2
    GOSPA距离1.13031.0671
    下载: 导出CSV

    表  3  目标长短轴的GOSPA距离统计平均值(m)

    方案1方案2
    GOSPA距离1.55941.5009
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-09
  • 录用日期:  2022-06-22
  • 修回日期:  2022-06-02
  • 网络出版日期:  2022-06-29
  • 刊出日期:  2023-01-17

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