AI 中文总结
本文提出一种结合Maronna稳健估计与尖峰协方差建模的MVDR波束成形方法，利用随机矩阵理论推导最优收缩权重，在高维重尾噪声下实现更强的干扰抑制和更高输出SINR。
AI 中文摘要
本文提出了一种针对重尾噪声污染的阵列观测的稳健高维最小方差无失真响应（MVDR）波束成形方法。所提方法通过将Maronna稳健散度估计器与尖峰协方差建模相结合，构建了一个面向MVDR的精度矩阵估计器。利用随机矩阵理论工具，我们推导了MVDR输出功率的确定性等效，并获得了主信号子空间的渐近最优收缩权重。随后，开发了一种完全基于样本的实现方法，用于实际波束成形器设计。在椭圆分布噪声下的数值模拟表明，在高维和脉冲噪声场景中，所提波束成形器比竞争方法实现了更强的干扰抑制和更高的输出信干噪比（SINR）。
英文摘要
This paper proposes a robust high-dimensional minimum variance distortionless response (MVDR) beamforming method for array observations corrupted by heavy-tailed noise. The proposed approach constructs an MVDR-oriented precision matrix estimator by combining Maronna's robust scatter estimator with spiked covariance modeling. Using tools from random matrix theory, we derive a deterministic equivalent of the MVDR output power and obtain asymptotically optimal shrinkage weights for the dominant signal subspace. A fully sample-based implementation is then developed for practical beamformer design. Numerical simulations under elliptically distributed noise demonstrate that the proposed beamformer achieves stronger interference suppression and higher output SINR than competing methods in high-dimensional and implusive noise regimes.
