The performance of robust adaptive beamforming (RAB) based on interference-plus-noise covariance (IPNC) matrix reconstruction can be degraded seriously in the presence of random mismatches (look direction and array geometry), particularly when the input signal-to-noise ratio (SNR) is high. In this work, we present a RAB technique to address covariance matrix reconstruction problems. The proposed RAB technique involves IPNC matrix reconstruction using a low-complexity spatial sampling process (LCSSP) and employs a virtual received array vector. In particular, the power spectrum sampling is realized by a proposed projection matrix in a higher dimension. The essence of the proposed technique is to avoid reconstruction of the IPNC matrix by integrating over the angular sector of the interference-plus-noise region. Simulation results are presented to verify the effectiveness of the proposed RAB approach.

基于干扰加噪声协方差 (IPNC) 矩阵重构的鲁棒自适应波束成形 (RAB) 的性能在存在随机失配（观察方向和阵列几何形状）的情况下会严重降低，特别是当输入信噪比(SNR) 高。在这项工作中，我们提出了一种 RAB 技术来解决协方差矩阵重建问题。建议的 RAB 技术涉及使用低复杂度空间采样过程 (LCSSP) 的 IPNC 矩阵重建，并采用虚拟接收阵列向量。特别地，功率谱采样是通过提出的更高维度的投影矩阵来实现的。所提出技术的本质是通过在干扰加噪声区域的角扇区上进行积分来避免重建 IPNC 矩阵。