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基于线性约束最小方差的稳健波束形成算法

吕岩 曹菲

吕岩,曹菲. 基于线性约束最小方差的稳健波束形成算法[J]. 北京航空航天大学学报,2023,49(3):617-624 doi: 10.13700/j.bh.1001-5965.2021.0280
引用本文: 吕岩,曹菲. 基于线性约束最小方差的稳健波束形成算法[J]. 北京航空航天大学学报,2023,49(3):617-624 doi: 10.13700/j.bh.1001-5965.2021.0280
LYU Y,CAO F. Robust beamforming based on linear constraint minimum variance algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(3):617-624 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0280
Citation: LYU Y,CAO F. Robust beamforming based on linear constraint minimum variance algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(3):617-624 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0280

基于线性约束最小方差的稳健波束形成算法

doi: 10.13700/j.bh.1001-5965.2021.0280
详细信息
    通讯作者:

    E-mail:caofei101@126.com

  • 中图分类号: TN957.2

Robust beamforming based on linear constraint minimum variance algorithm

More Information
  • 摘要:

    针对平面阵列天线波束形成过程中的波达方向(DOA)估计失配问题,提出在期望信号(SOI)方向附近增加线性约束的算法,有效提升了平面阵列波束形成的稳健性;此外,针对增加线性约束会导致波束形成算法自由度降低的问题,以均匀线阵为例,提出在广义旁瓣相消(GSC)算法模型中添加阻塞矩阵预选环节的算法,有效解决了添加线性约束所致的自由度损失问题,从而使算法在提升稳健性的同时保持了原有的自由度。最后,通过计算机仿真实验验证了所提算法的有效性。

     

  • 图 1  平面阵列

    Figure 1.  Planar array

    图 2  GSC模型

    Figure 2.  Model of GSC

    图 3  字典示意图

    Figure 3.  Sign for dictionary

    图 4  改进GSC模型

    Figure 4.  Model of improved GSC

    图 5  波束增益

    Figure 5.  Pattern synthesis

    图 6  等高线

    Figure 6.  Contour plot

    图 7  方位角15o波束截面

    Figure 7.  Sectional drawing of 15o

    图 8  波束畸变

    Figure 8.  Pattern synthesis distortion

    图 9  DOA失配增益

    Figure 9.  Pattern synthesis of DOA mismatch

    图 10  平面阵波束增益

    Figure 10.  Pattern synthesis of planar array

    图 11  平面阵等高线

    Figure 11.  Contour plot of planar array

    图 12  不同方位角的波束截面

    Figure 12.  Sectional drawings of pattern syntheses for different azimuths

    图 13  范数变化趋势

    Figure 13.  Trends of norms

    图 14  改进GSC和文献[7]波束形成对比

    Figure 14.  Comparison of pattern synthesis between improved GSC and Ref. [7]

    图 15  不同参数时的范数变化趋势

    Figure 15.  Trends of norms under different parameters

    图 16  运行时间对比

    Figure 16.  Comparison of running times

    表  1  不同阵元数量耗时

    Table  1.   Time consumed versus different elements

    算法不同阵元数量耗时/ms
    10306080100
    GSC0.18060.38940.67750.90291.2060
    文献[7]0.21270.45880.75481.15051.3563
    改进GSC0.27550.90261.66342.40043.2786
    下载: 导出CSV
  • [1] WEI Z, LI X, WANG B, et al. An efficient super-resolution DOA estimator based on grid learning[J]. Radio Engineering, 2019, 28(4): 785-792.
    [2] YANG X P, LI S, SUN Y, et al. Robust wideband adaptive beamforming with null broadening and constant beamwidth[J]. IEEE Transactions on Antennas and Propagation, 2019, 67(8): 5380-5389. doi: 10.1109/TAP.2019.2916607
    [3] AKBAR S, RAJA M A Z, ZAMAN F, et al. Design of bio-inspired heuristic techniques hybridized with sequential quadratic programming for joint parameters estimation of electromagnetic plane waves[J]. Wireless Personal Communications, 2017, 96: 1475-1494. doi: 10.1007/s11277-017-4251-y
    [4] TALISA S H, OHAVER K W, COMBERIATE T M, et al. Benefits of digital phased array radars[J]. Proceedings of the IEEE, 2016, 104(3): 530-543. doi: 10.1109/JPROC.2016.2515842
    [5] SCHULDT C. Trigonometric interpolation beamforming for a circular microphone array[C]//IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE Press, 2019: 431-435.
    [6] SHERSON T, KLEIJN W B, HEUSDENS R. A distributed algorithm for robust LCMV beamforming[C]//IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE Press, 2016: 101-105.
    [7] KHAN M, MALIK A N, ZAMAN F, et al. Robust LCMV beamformer for direction of arrival mismatch without beam broadening[J]. Wireless Personal Communications, 2019, 104: 21-36. doi: 10.1007/s11277-018-6006-9
    [8] CAMELLIA D, SIEH K T. Performance comparison of FA, PSO and CS application in SINR optimisation for LCMV beamforming technique[J]. Wireless Personal Communications, 2018, 103: 2177-2195.
    [9] WANG W, WU R. A novel diagonal loading method for robust adaptive beamforming[J]. Progress in Electromagnetics Research C, 2011, 18: 245-255. doi: 10.2528/PIERC10091803
    [10] VOROBYOV S A, GERSHMAN A B, LUO Z Q. Robust adaptive beamforming using worst-case performance optimization: A solution to the signal mismatch problem[J]. IEEE Transactions on Signal Processing, 2003, 51(2): 313-324. doi: 10.1109/TSP.2002.806865
    [11] 王博, 谢军伟, 张晶, 等. 基于SD-LCMV算法的FDA平台外干扰抑制[J]. 北京航空航天大学学报, 2019, 45(11): 2247-2256. doi: 10.13700/j.bh.1001-5965.2019.0140

    WANG B, XIE J W, ZHANG J, et al. FDA platform external interference suppression based on SD-LCMV algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2247-2256(in Chinese). doi: 10.13700/j.bh.1001-5965.2019.0140
    [12] 王永良, 丁前军, 李荣峰. 自适应阵列处理[M]. 北京: 清华大学出版社, 2009: 44-47.

    WANG Y L, DING Q J, LI R F. Adaptive array processing[M]. Beijing: Tsinghua University Press, 2009: 44-47(in Chinese).
    [13] WANG P, SHI Y, JIANG J, et al. Generalized sidelobe canceller for ultrasound imaging based on eigenvalue decomposition[J]. Acoustical Physics, 2019, 65(1): 123-131. doi: 10.1134/S1063771019010159
    [14] QIN Y H, LIU Y M, LIU J Y, et al. Underdetermined wideband doa estimation for off-grid sources with coprime array using sparse bayesian learning[J]. Sensors, 2018, 18(1): 253-263.
    [15] YUAN J W, ZHANG G, LIU W B, et al. Off-grid sparse DOA estimation based iterative reweighted linear interpolation in spatial coloured noise[J]. Electronics Letters, 2020, 56(11): 573-575. doi: 10.1049/el.2020.0381
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  • 被引次数: 0
出版历程
  • 收稿日期:  2021-05-28
  • 录用日期:  2021-08-29
  • 网络出版日期:  2021-09-14
  • 整期出版日期:  2023-03-30

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