Robust adaptive beamforming (RAB) based on interference-plus-noise covariance (INC) matrix reconstruction can experience performance degradation when model mismatch errors exist, particularly when the input signal-to-noise ratio (SNR) is large. In this work, we devise an efficient RAB technique for dealing with covariance matrix reconstruction issues. The proposed method involves INC matrix reconstruction using an idea in which the power and the steering vector of the interferences are estimated based on the power method. Furthermore, spatial match processing is computed to reconstruct the desired signal-plus-noise covariance matrix. Then, the noise components are excluded to retain the desired signal (DS) covariance matrix. A key feature of the proposed technique is to avoid eigenvalue decomposition of the INC matrix to obtain the dominant power of the interference-plus-noise region. Moreover, the INC reconstruction is carried out according to the definition of the theoretical INC matrix. Simulation results are shown and discussed to verify the effectiveness of the proposed method against existing approaches.

翻译：鲁棒自适应波束形成（RAB）技术依赖于干扰加噪声协方差（INC）矩阵重构，但在模型失配误差存在时，尤其当输入信噪比（SNR）较大时，可能出现性能退化。本文提出了一种有效的RAB技术以解决协方差矩阵重构问题。该方法采用基于功率法估计干扰功率与导向向量的思路实现INC矩阵重构。进一步通过计算空间匹配处理重构期望信号加噪声协方差矩阵，并剔除噪声分量以保留期望信号（DS）协方差矩阵。该技术的核心特点在于避免对INC矩阵进行特征分解以获取干扰加噪声区域的主导功率，同时根据理论INC矩阵的定义进行重构。仿真结果展示与讨论验证了所提方法相较现有方案的有效性。