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Robust adaptive beamforming based on a method for steering vector estimation and interference covariance matrix reconstruction

机译：基于转向载波估计和干扰协方差矩阵重建方法的鲁棒自适应波束形成

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摘要

Robustness is an important factor in adaptive beamforming because various mismatches that exist in reality will lead to considerable performance degradation. In this paper, a robust adaptive beamforming (RAB) algorithm is proposed based on a novel method for estimating the steering vectors (SVs). The interference-plus-noise covariance matrix (1NCM) is then reconstructed by the SVs and their corresponding power estimates. As we know, the Capon power spectrum is actually a function of the SV defined in a high-dimensional domain, in which the actual SVs correspond to the highest peaks. The nominal SVs may lead to relatively lower power amplitude points around the peaks when mismatches exist, and these peaks are located in the directions of gradient vectors at the lower power points obtained by the nominal SVs. Therefore, to obtain the actual SVs, we first construct a subspace for each nominal SV in a small neighborhood of angles. Then we get the gradient vector, which is orthogonal to the corresponding nominal SV neighborhood, using a subspace-based method. Finally, we search along the gradient vector to obtain the adjusted SV that generates the highest Capon power amplitude. The interference covariance matrix (ICM) is reconstructed by the adjusted interference SVs and corresponding Capon power amplitudes. The actual SV of the signal of interest (SOI) is estimated as the adjusted SV of the SOI. Simulation results demonstrate that the proposed method is robust against various types of mismatch and is superior to other existing reconstruction-based beamforming algorithms.
机译：鲁棒性是自适应波束成形中的一个重要因素，因为现实中存在的各种不匹配会导致相当大的性能下降。本文基于用于估计转向矢量（SVS）的新方法，提出了一种鲁棒自适应波束形成（RAB）算法。然后由SV和它们的相应功率估计重建干扰加噪声协方差矩阵（1ncm）。如我们所知，Capon功率谱实际上是在高维域中定义的SV的函数，其中实际SVS对应于最高峰值。当存在不匹配时，标称SVS可以导致峰周围的功率幅度点，并且这些峰位于由标称SV获得的较低功率点处的梯度矢量的方向上。因此，为了获得实际的SVS，我们首先在角度的小邻域内构建每个标称SV的子空间。然后，我们使用基于子空间的方法获得与相应的标称SV邻域正交的梯度向量。最后，我们沿着梯度向量搜索以获得产生最高的Capon功率幅度的调整后的SV。通过调整的干扰SV和相应的CAPON功率幅度重建干扰协方差矩阵（ICM）。感兴趣信号（SOI）的实际SV被估计为SOI的调整后的SV。仿真结果表明，该方法对各种类型的不匹配是鲁棒的，并且优于其他基于重建的基于波束形成算法。

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  • 来源
    《Signal processing》 |2021年第5期|107939.1-107939.8|共8页
  • 作者

    Sicong Sun; Zhongfu Ye;

  • 作者单位

    Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei Anhui 230027 China National Engineering Laboratory for Speech and Language Information Processing Hefei Anhui 230027 China;

    Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei Anhui 230027 China National Engineering Laboratory for Speech and Language Information Processing Hefei Anhui 230027 China;

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  • 正文语种 eng
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  • 关键词

    Robust adaptive beamforming; Interference covariance matrix reconstruction; Subspace decomposition; Steering vector estimation;

    机译：强大的自适应波束形成;干扰协方差矩阵重建;子空间分解;转向矢量估计;

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