This paper presents an uncertainty-set-shrinkage (USS) algorithm that aims to reconstruct a precise interference-plus-noise covariance matrix (INCM) and improve the performance of adaptive beamformers when steering vector (SV) mismatch exists. Both of the interference covariance matrix (ICM) and the desired signal covariance matrix (DSCM) can be divided into two parts, namely the nominal matrix reconstructed using the nominal SVs and the error matrix consisting of the residual component of the covariance matrix. By using a two-step uncertainty set shrinkage method, the proposed beamformer constructs the error matrices by integrating the estimated spatial spectrum over the shrinked uncertainty set at a cost of low computational complexity. After extracting the principal component of the reconstructed ICM and DSCM, the INCM and the SV of the source of interest (SOI) can be estimated without solving any optimization problem. Both of numerical simulations and experimental results demonstrate that the performance of the proposed algorithm is robust with several categories of SV mismatches.

中文翻译：

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一种用于鲁棒自适应波束成形的基于不确定集收缩的协方差矩阵重构算法

本文提出了一种不确定性集收缩 (USS) 算法，旨在重建精确的干扰加噪声协方差矩阵 (INCM) 并在存在导向向量 (SV) 失配时提高自适应波束成形器的性能。干扰协方差矩阵（ICM）和期望信号协方差矩阵（DSCM）都可以分为两部分，即使用标称SV重构的标称矩阵和由协方差矩阵的残差分量组成的误差矩阵。通过使用两步不确定性集收缩方法，所提出的波束形成器通过以较低的计算复杂度为代价在收缩的不确定性集上整合估计的空间谱来构建误差矩阵。提取重构后的ICM和DSCM的主成分后，可以在不解决任何优化问题的情况下估计感兴趣源 (SOI) 的 INCM 和 SV。数值模拟和实验结果都表明，所提出算法的性能对几种类别的 SV 失配具有鲁棒性。