任务驱动的自组织蜂群柔性阵列波束赋形算法研究

梁军利 涂宇 马云红 李立欣 陈永红 方学立

梁军利, 涂宇, 马云红, 等. 任务驱动的自组织蜂群柔性阵列波束赋形算法研究[J]. 雷达学报, 2022, 11(4): 517–529. doi: 10.12000/JR22130
引用本文: 梁军利, 涂宇, 马云红, 等. 任务驱动的自组织蜂群柔性阵列波束赋形算法研究[J]. 雷达学报, 2022, 11(4): 517–529. doi: 10.12000/JR22130
LIANG Junli, TU Yu, MA Yunhong, et al. Task-driven flexible array beampattern synthesis for self-organized drone swarm[J]. Journal of Radars, 2022, 11(4): 517–529. doi: 10.12000/JR22130
Citation: LIANG Junli, TU Yu, MA Yunhong, et al. Task-driven flexible array beampattern synthesis for self-organized drone swarm[J]. Journal of Radars, 2022, 11(4): 517–529. doi: 10.12000/JR22130

任务驱动的自组织蜂群柔性阵列波束赋形算法研究

doi: 10.12000/JR22130
基金项目: 国家自然科学基金(62271403),陕西省重点研发项目(2021SF-166)
详细信息
    作者简介:

    梁军利(1978-),男,陕西咸阳人,博士,西北工业大学电子信息学院教授。目前在IEEE及Elsevier系列期刊发表学术论文60多篇。主要研究方向为雷达信号处理、阵列信号处理、通信感知一体化、光电融合及目标识别等

    涂 宇(1999-),女,贵州遵义人,西北工业大学硕士研究生。研究方向为雷达波形设计、阵列信号处理等

    马云红(1972-),女,博士,西北工业大学电子信息学院副教授。主要研究方向为无人机协同目标探测、无人机系统任务规划、复杂系统建模、智能优化算法等

    李立欣(1979-),男,陕西西安人,博士,教授。2008年在西北工业大学获得博士学位。在国内外顶级期刊和会议发表学术论文150多篇。主要研究方向为5G/6G无线通信、通信感知一体化、无人机通信与组网等

    通讯作者:

    梁军利 liangjunli@nwpu.edu.cn

  • 责任主编:崔国龙 Corresponding Editor: CUI Guolong
  • 中图分类号: TN957.51

Task-driven Flexible Array Beampattern Synthesis for Self-organized Drone Swarm

Funds: The National Nature Science Foundation of China (62271403), Key Research and Development Program of Shaanxi (2021SF-166)
More Information
  • 摘要: 根据无人机蜂群构型自组织调整位置和权向量能够实现波束指向特定方向的任务需求,该文提出了一种新颖的任务驱动的自组织蜂群柔性阵列波束赋形算法。首先,建立以无人机蜂群距离为约束、以无人机机载天线坐标位置及权向量为优化变量的波束赋形数学模型。接着,应用Lawson准则简化目标函数,将天线坐标位置及权向量的两类变量优化问题简化为天线坐标位置的单类变量优化,解决了波束赋形模型优化变量耦合带来的求解难题。同时,引入辅助变量,进行约束和复杂目标函数的分离,并通过交替方向乘子法进行求解,降低了包含约束的高度非线性优化问题的求解难度。此外,该文将上述算法扩展至目标方向不精确的应用场景。仿真结果表明,该方法可有效降低波束赋形峰值旁边电平。

     

  • 图  1  蜂群不调整位置时进行波束赋形结果

    Figure  1.  Results of beamforming when the swarm does not adjust its position

    图  2  蜂群位置和权向量联合优化结果

    Figure  2.  Joint optimization results of swarm position and weight vector

    图  3  算法1的对比算法所得结果

    Figure  3.  Comparison of the results of algorithm 1

    图  4  算法1鲁棒性测试

    Figure  4.  Robustness test of algorithm 1

    算法1 任务驱动的自组织蜂群柔性阵列波束赋形
    Alg. 1 Task-driven flexible array beampattern synthesis for self-organized drone swarm
     步骤1  随机初始化无人机坐标位置$\left\{ { {{\boldsymbol{p}}_n}(0)} \right\}$;设定波束指向方
         向$ ({\theta _0},{\phi _0}) $和干扰方向$ ({\theta _l},{\phi _l}) $;初始化Lawson权值
         $ {v_t}\left( {\theta ,\phi } \right) $为1,即$ {v_0}\left( {\theta ,\phi } \right) = 1,{\text{ }}\forall \left( {\theta ,\phi } \right) \in {\rm {Sidelobe}} $;设
         外循环迭代变量为t;最大外循环次数$ {T_0} $;
     步骤2  随机初始化$ \{ {{\boldsymbol{q}}_n}(0),{{\boldsymbol{\lambda }}_n}(0)\} $,设内循环迭代变量为k;最
         大内循环次数$ {K_0} $;根据式(9)计算权值矢量$ {{\boldsymbol{w}}_t} $;
         步骤2.1 应用式(13),式(14)确定$ \left\{ {{{\boldsymbol{p}}_n}(k + 1)} \right\}_{n = 1}^N $;
         步骤2.2 应用式(17)确定$ \left\{ {{{\boldsymbol{q}}_n}(k + 1)} \right\}_{n = 1}^N $;
         步骤2.3 应用式(18)更新拉格朗日乘子;
         步骤2.4 重复步骤2.1—步骤2.3,直至达到最大迭代次
             数$ {K_0} $;
     步骤3  应用式(5)进行权值更新;
     步骤4  重复步骤2、步骤3,直至达到最大迭代次数$ {T_0} $。
     输出:无人机权矢量$ {{\boldsymbol{w}}_t} $和坐标$ \left\{ {{{\boldsymbol{p}}_n}(k)} \right\} $
    下载: 导出CSV
    算法2 目标方向不精确时的自组织蜂群柔性阵列波束赋形
    Alg. 2 Flexible array beampattern synthesis for self-organized drone swarm with imprecise target direction
     步骤1 初始化$ \{ {\boldsymbol{w}}(0),{{\boldsymbol{p}}_n}(0),{\lambda _m}(0),{\kappa _s}(0)\} $;循环迭代变量t
         最大循环次数$ {T_0} $;
     步骤2 执行式(23)—式(25)获得$ \{ \varepsilon (t + 1),{u_m}(t + 1),{v_s}(t + 1)\} $;
     步骤3 执行式(26)—式(36)获得$ {\boldsymbol{w}}(t + 1),{{\boldsymbol{p}}_n}(t + 1) $;
     步骤4 执行式(37),式(38)进行拉格朗日乘子更新;
     重复步骤2—步骤4,直至达到最大迭代次数$ {T_0} $。
     输出:无人机权矢量$ {{\boldsymbol{w}}_t} $和坐标$ \left\{ {{{\boldsymbol{p}}_n}(k)} \right\} $
    下载: 导出CSV

    表  1  算法1天线个数变化时PSL对比

    Table  1.   PSL comparison when the number of antennas changes in algorithm 1

    天线个数仅优化权向量时的PSL (dB)联合优化时的PSL (dB)
    50–13.27–15.56
    100–16.33–23.13
    150–20.50–25.21
    下载: 导出CSV

    表  2  算法2天线个数变化时PSL对比

    Table  2.   PSL comparison when the number of antennas changes in algorithm 2

    天线个数仅优化权向量时的PSL (dB)联合优化时的PSL (dB)
    50–5.55–15.82
    100–10.76–24.45
    150–19.39–28.04
    下载: 导出CSV

    表  3  算法1不同距离约束下PSL对比

    Table  3.   Comparison of PSL under different distance constraints in algorithm 1

    距离约束PSL (dB)距离约束PSL (dB)
    $0.2 \times \dfrac{\lambda}{2}$–20.43$2 \times \dfrac{\lambda}{2} $–23.58
    $0.5 \times \dfrac{\lambda}{2}$–19.16$5 \times \dfrac{\lambda}{2} $–22.35
    $1 \times \dfrac{\lambda}{2} $–23.87
    下载: 导出CSV

    表  4  算法2不同距离约束下PSL对比

    Table  4.   Comparison of PSL under different distance constraints in algorithm 2

    距离约束PSL (dB)距离约束PSL (dB)
    $0.2 \times \dfrac{\lambda}{2}$–14.65$2 \times \dfrac{\lambda}{2}$–17.27
    $0.5 \times \dfrac{\lambda}{2}$–21.34$5 \times \dfrac{\lambda}{2}$–11.73
    $1 \times \dfrac{\lambda}{2}$–24.45
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-07-01
  • 修回日期:  2022-08-12
  • 网络出版日期:  2022-08-23
  • 刊出日期:  2022-08-28

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