论文标题
低成本的最大熵协方差矩阵重建算法可稳健自适应波束成形
Low-Cost Maximum Entropy Covariance Matrix Reconstruction Algorithm for Robust Adaptive Beamforming
论文作者
论文摘要
在这封信中，我们使用随机梯度算法提出了一种新型的低复杂性自适应波束形成技术，以避免矩阵反转。所提出的方法基于最大熵功率谱（MEP）利用算法来估计噪声加干关协方差矩阵（MEPS-NPIC），以使波束成形的权重适应性更新，从而大大降低了计算复杂性。 MEPs进一步用于重建所需的信号协方差矩阵，并改善所需信号转向向量（SV）的估计。仿真显示了所提出的MEPS-NPIC方法的优越性，而不是先前提出的波束形式。
In this letter, we present a novel low-complexity adaptive beamforming technique using a stochastic gradient algorithm to avoid matrix inversions. The proposed method exploits algorithms based on the maximum entropy power spectrum (MEPS) to estimate the noise-plus-interference covariance matrix (MEPS-NPIC) so that the beamforming weights are updated adaptively, thus greatly reducing the computational complexity. MEPS is further used to reconstruct the desired signal covariance matrix and to improve the estimate of the desired signals's steering vector (SV). Simulations show the superiority of the proposed MEPS-NPIC approach over previously proposed beamformers.
