通过空间采样重构协方差矩阵，提升波束成形对误差的鲁棒性。
Covariance Matrix Construction with Preprocessing-Based Spatial Sampling for Robust Adaptive Beamforming
- 基于预处理的空间采样法自适应估计干扰方向并重建干扰加噪声协方差矩阵。
- 用样本协方差矩阵替代预处理矩阵，在收缩方法中实现计算简化。
- 适用于存在信号方向偏差的复杂电磁环境，适合雷达与通信系统应用。
本文提出一种高效且鲁棒的自适应波束成形技术，以应对方向矢量（SV）估计偏差及数据协方差矩阵重构问题。首先，利用可用快照自适应估计干扰源的到达方向（DoA），并计算其角度区域；随后，采用经典的线性组合算法，结合基于预处理的空间采样（PPBSS）方法重构干扰加噪声协方差（IPNC）矩阵。研究发现，预处理矩阵可在收缩方法中被样本协方差矩阵（SCM）替代。进一步基于估计的角度区域信息设计了功率谱采样策略，并在信号关注区域（SOI）形成信号协方差矩阵，进而通过幂迭代法求解SOI的方向矢量。本文分析了所提方法的阵列波束图特性，并对比了多种方法的计算成本。仿真结果表明，该方法在性能上优于现有技术。
原文摘要 · Abstract (English)
This work proposes an efficient, robust adaptive beamforming technique to deal with steering vector (SV) estimation mismatches and data covariance matrix reconstruction problems. In particular, the direction-of-arrival(DoA) of interfering sources is estimated with available snapshots in which the angular sectors of the interfering signals are computed adaptively. Then, we utilize the well-known general linear combination algorithm to reconstruct the interference-plus-noise covariance (IPNC) matrix using preprocessing-based spatial sampling (PPBSS). We demonstrate that the preprocessing matrix can be replaced by the sample covariance matrix (SCM) in the shrinkage method. A power spectrum sampling strategy is then devised based on a preprocessing matrix computed with the estimated angular sectors' information. Moreover, the covariance matrix for the signal is formed for the angular sector of the signal-of-interest (SOI), which allows for calculating an SV for the SOI using the power method. An analysis of the array beampattern in the proposed PPBSS technique is carried out, and a study of the computational cost of competing approaches is conducted. Simulation results show the proposed method's effectiveness compared to existing approaches.
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