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  • 李鹏,夏翔,俞传富,宋逸君. 基于导向矢量估计的鲁棒波束形成[J]. 雷达科学与技术, 2020, 18(1): 21-26.    [点击复制]
  • LI Peng,XIA Xiang,YU Chuanfu,SONG Yijun. Robust Beamforming Based on Steering Vector Estimation[J]. Radar Science and Technology, 2020, 18(1): 21-26.   [点击复制]
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基于导向矢量估计的鲁棒波束形成
李鹏,夏翔,俞传富,宋逸君
0
(南京信息工程大学江苏省气象探测与信息处理重点实验室,江苏南京210044)
摘要:
针对标准Capon波束形成器中真实导向矢量与期望导向矢量存在误差时,其性能会急剧下降的问题,提出了基于加权空间平滑与导向矢量估计相结合的鲁棒波束形成算法。该算法利用加权空间平滑方法,对子阵进行特殊的划分,根据子阵间自相关矩阵与互相关矩阵权重差异,采用嵌套的方式获得加权矩阵,继而得到更加精确的协方差矩阵,接着,使用不确定范围约束期望导向矢量来获得真实导向矢量。仿真结果表明,和传统的自适应波束形成算法相比较,本文算法在面对协方差矩阵中含有期望信号以及角度失配问题时,鲁棒性得到明显提升。
关键词:  针对标准Capon波束形成器中真实导向矢量与期望导向矢量存在误差时,其性能会急剧下降的问题,提出了基于加权空间平滑与导向矢量估计相结合的鲁棒波束形成算法。该算法利用加权空间平滑方法,对子阵进行特殊的划分,根据子阵间自相关矩阵与互相关矩阵权重差异,采用嵌套的方式获得加权矩阵,继而得到更加精确的协方差矩阵,接着,使用不确定范围约束期望导向矢量来获得真实导向矢量。仿真结果表明,和传统的自适应波束形成算法相比较,本文算法在面对协方差矩阵中含有期望信号以及角度失配问题时,鲁棒性得到明显提升。
DOI:DOI:10.3969/j.issn.1672-2337.2020.01.004
基金项目:江苏省第11批六大高峰人才项目(No.2014 XXRJ 006);国家自然科学基金(No.41075115)
Robust Beamforming Based on Steering Vector Estimation
LI Peng,XIA Xiang,YU Chuanfu,SONG Yijun
(Jiangsu Key Laboratory of Meteorological Observation and Information Procession,Nanjing University of Information Science and Technology,Nanjing 210044,China)
Abstract:
In order to solve the problem that the performance of standard Capon beamformer will decline sharply when there are errors between the real and expected steering vectors,a robust beamforming algorithm based on weighted spatial smoothing and steering vector estimation is proposed. The weighted space smoothing method is used to partition the subarrays. According to the weight difference between the autocorrelation matrix and the crosscorrelation matrix,the weighted matrix is obtained by nesting,and then the more accurate covariance matrix is obtained. Then,the desired steering vector with uncertain range constraints is used to obtain the real steering vector. The simulation results show that the robustness of the proposed algorithm is significantly improved when the covariance matrix contains the desired signal and the angle mismatch problem in comparison with the traditional adaptive beamforming algorithm.
Key words:  weighted spatial smoothing  adaptive beamforming  steering vector estimation  robustness