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基于均匀圆阵矢量传感器的DOA和极化参数联合估计

时春鹏 何华锋 何耀民 李震 闫少强 张孝宇

时春鹏,何华锋,何耀民,等. 基于均匀圆阵矢量传感器的DOA和极化参数联合估计[J]. 北京航空航天大学学报,2024,50(4):1325-1335 doi: 10.13700/j.bh.1001-5965.2022.0390
引用本文: 时春鹏,何华锋,何耀民,等. 基于均匀圆阵矢量传感器的DOA和极化参数联合估计[J]. 北京航空航天大学学报,2024,50(4):1325-1335 doi: 10.13700/j.bh.1001-5965.2022.0390
SHI C P,HE H F,HE Y M,et al. Joint estimation of DOA and polarization parameters based on uniform circle array with vector sensor[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(4):1325-1335 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0390
Citation: SHI C P,HE H F,HE Y M,et al. Joint estimation of DOA and polarization parameters based on uniform circle array with vector sensor[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(4):1325-1335 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0390

基于均匀圆阵矢量传感器的DOA和极化参数联合估计

doi: 10.13700/j.bh.1001-5965.2022.0390
基金项目: 国家自然科学基金(61573366)
详细信息
    通讯作者:

    E-mail:hhf0903@163.com

  • 中图分类号: TN911.7

Joint estimation of DOA and polarization parameters based on uniform circle array with vector sensor

Funds: National Natural Science Foundation of China (61573366)
More Information
  • 摘要:

    针对极化敏感阵列中非完备电磁矢量传感器多参数联合估计问题,提出一种基于三维电磁矢量传感器平面圆阵模型的波达方向(DOA)和极化参数联合估计方法。利用阵列流型矩阵的特性将四维谱函数进行解耦,严格证明DOA搜索与极化参数估计的不相关性,将四维谱搜索优化为仅与DOA相关的二维谱搜索,提出DOA谱峰搜索二级比较策略,可使雷达自动识别谱峰坐标,有效提高信号处理的实时性;提出一种基于精英反向学习和Lévy飞行的改进粒子群算法对极化参数进行估计,提高极化参数的收敛性能。通过与同类算法进行仿真对比,结果表明:所提算法在牺牲少量时间的条件下可提高估计精度。

     

  • 图 1  入射波与圆阵中心的示意图

    Figure 1.  Schematic diagram of incident wave and center of circle array

    图 2  电场的正交分解示意图

    Figure 2.  Schematic diagram of orthogonal decomposition of electric field

    图 3  DOA谱峰搜索二级比较策略

    Figure 3.  Two level comparison strategy of DOA peak search

    图 4  信号的DOA估计

    Figure 4.  DOA estimation of signals

    图 5  DOA估计等高线俯视图

    Figure 5.  Top view of DOA estimation contour line

    图 7  Tk(γ,η)的等高线俯视图及粒子的初始化分布(N=20)

    Figure 7.  Top view of Tk(γ,η) contour, and initial distribution of particles (N=20)

    图 6  Tk(γ,η)的三维空间图

    Figure 6.  3D space map of Tk(γ,η)

    图 8  Tk(γ,η)的适应度进化曲线

    Figure 8.  Fitness evolution curve of Tk(γ,η)

    图 10  俯仰角φ随SNR的RMSE

    Figure 10.  RMSE of pitch angle φ with respect to SNR

    图 11  极化幅角γ随SNR的RMSE

    Figure 11.  RMSE of polarization amplitude angle φ with respect to SNR

    图 9  方位角θ随SNR的RMSE

    Figure 9.  RMSE of azimuth angle θ with respect to SNR

    图 12  极化相位差η随SNR的RMSE

    Figure 12.  RMSE of polarization phase angle η with respect to SNR

    表  1  不同信源数下识别谱峰所需时间

    Table  1.   Time required to identify spectral peaks with different numbers of sources s

    信源数 所需时间
    人为输入 采用DOA二级比较策略
    1 约3 2×10 −3
    2 约6 7.4×10 −3
    3 约9 7.6×10 −3
    4 约12 8.2×10 −3
    5 约15 18.4×10 −3
    下载: 导出CSV

    表  2  不同粒子数的PSO算法和本文算法收敛性能

    Table  2.   Convergence performance between PSO algorithm and the proposed algorithm with different particle numbers

    N Cinteration Ctime/s Cvalue
    PSO算法 本文算法 PSO算法 本文算法 PSO算法 本文算法
    10 25.6 13.4 0.0059 0.0082 0.0076 0.0071
    15 17.86 10.66 0.0067 0.0091 0.0066 0.0062
    20 16.8 8.66 0.0085 0.0099 0.0054 0.0053
    25 15.73 8.46 0.0103 0.0116 0.0054 0.0053
    30 13.8 8.53 0.0108 0.0129 0.0054 0.0053
    下载: 导出CSV

    表  3  不同数据类型进行乘、加运算产生的运算量

    Table  3.   Amount of computation generated by multiplication and addition of different data types

    数据类型 运算类型 运算量/次
    实数 复数 四元数
    实数 相乘 1(乘法)
    相加 1(加法)
    复数 相乘 2(乘法) 4(乘法)
    2(加法)
    8(乘法)
    4(加法)
    相加 1(加法) 2(加法) 2(加法)
    四元数 相乘 16次(乘法)
    12次(加法)
    相加 4(加法)
    下载: 导出CSV

    表  4  MUSIC类算法运算性能

    Table  4.   Computational performance among 4 MUSIC-type algorithms

    算法 D $ {\widehat {\boldsymbol{R}}_{\boldsymbol{X}}} $运算量/次 $ (\theta ,\varphi ) $运算量/次 $ (\gamma ,\eta ) $搜索时间/s 总运行时间/s
    LV-MUSIC 6 1.4746×107(乘法)
    7.3728×106(加法)
    7.2402×1012(乘法)
    3.6200×1012(加法)
    46047.9
    DL-MUSIC 2 1.6384×106(乘法)
    8.1920×105(加法)
    4.7369×107(乘法)
    2.6984×107(加法)
    0.0108 1.3044
    Q-MUSIC 2 1.6384×106(乘法)
    8.1920×105(加法)
    4.5166×107(乘法)
    2.2258×107(加法)
    0.1604 1.5627
    本文算法 3 3.6864×106(乘法)
    1.8432×106(加法)
    1.0672×108(乘法)
    5.3363×107(加法)
    0.0813 2.1682
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-05-19
  • 录用日期:  2022-08-19
  • 网络出版日期:  2022-09-14
  • 整期出版日期:  2024-04-29

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