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基于双层协方差矩阵重构的稳健波束形成算法

曹司磊 曾维贵 王磊

曹司磊, 曾维贵, 王磊等 . 基于双层协方差矩阵重构的稳健波束形成算法[J]. 北京航空航天大学学报, 2021, 47(8): 1605-1611. doi: 10.13700/j.bh.1001-5965.2020.0297
引用本文: 曹司磊, 曾维贵, 王磊等 . 基于双层协方差矩阵重构的稳健波束形成算法[J]. 北京航空航天大学学报, 2021, 47(8): 1605-1611. doi: 10.13700/j.bh.1001-5965.2020.0297
CAO Silei, ZENG Weigui, WANG Leiet al. A robust beamforming algorithm based on double-layer reconstruction of covariance matrix[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(8): 1605-1611. doi: 10.13700/j.bh.1001-5965.2020.0297(in Chinese)
Citation: CAO Silei, ZENG Weigui, WANG Leiet al. A robust beamforming algorithm based on double-layer reconstruction of covariance matrix[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(8): 1605-1611. doi: 10.13700/j.bh.1001-5965.2020.0297(in Chinese)

基于双层协方差矩阵重构的稳健波束形成算法

doi: 10.13700/j.bh.1001-5965.2020.0297
基金项目: 

装备预研领域基金 6140247030202

详细信息
    通讯作者:

    曾维贵. E-mail: 2230220154@qq.com

  • 中图分类号: TN911.7

A robust beamforming algorithm based on double-layer reconstruction of covariance matrix

Funds: 

Equipment Pre-Research Fund 6140247030202

More Information
  • 摘要:

    针对协方差矩阵含目标信号分量及目标导向矢量失配情况下,传统自适应波束形成器性能急剧下降的问题,提出了干扰加噪声协方差矩阵双层重构的稳健波束形成算法。首先,利用稀疏重构的方法预估干扰加噪声协方差矩阵,通过估计干扰导向矢量及干扰功率对干扰加噪声协方差矩阵进行优化校正;然后,基于子空间理论建立导向矢量约束误差优化模型,利用迭代方法对凸优化模型进行求解,得到最优权值向量。仿真结果表明:所提算法显著提高了波束形成器在目标导向矢量约束误差及阵列误差情况下的稳健性,低快拍条件下表现较好,输出性能优于仿真对比算法。

     

  • 图 1  算法求解流程

    Figure 1.  Flowchart for algorithm solution

    图 2  目标来波方向误差下不同算法平均输出SINR随输入SNR变化

    Figure 2.  SINR versus SNR for different algorithms under direction error of target signal

    图 3  目标来波方向误差下不同算法平均输出SINR随快拍数变化

    Figure 3.  SINR versus snapshot number for different algorithms under direction error of target signal

    图 4  阵列位置误差下不同算法平均输出SINR随输入SNR变化

    Figure 4.  SINR versus SNR for different algorithms under array position errors

    图 5  阵列位置误差下不同算法平均输出SINR随快拍数变化

    Figure 5.  SINR versus snapshot number for different algorithms under array position errors

    图 6  阵列互耦误差下不同算法平均输出SINR随输入SNR变化

    Figure 6.  SINR versus SNR for different algorithms under array mutual coupling errors

    图 7  阵列互耦误差下不同算法平均输出SINR随快拍数变化

    Figure 7.  SINR versus snapshot number for different algorithms under array mutual coupling errors

    图 8  算法迭代过程分析

    Figure 8.  Analysis of algorithm iteration process

    图 9  松弛因子对算法收敛速度影响

    Figure 9.  Influence of relaxation factor on convergence speed of algorithm

    表  1  收敛速度统计

    Table  1.   Convergence rate statistics

    Tmax 运行时间/ms
    γ=10-2 γ=10-3 γ=10-4 γ=10-5 γ=10-6
    20 71.4 91.1 121.8
    30 159.6
    40 243.3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-06-25
  • 录用日期:  2020-09-04
  • 刊出日期:  2021-08-20

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