冲击噪声背景下基于最小均方归一化误差的波束形成算法

 本文提出一种适用于任意未知统计特性的代数拖尾冲击噪声环境下的MMSNE波束形成算法。算法利用输出信号和参考信号之间的“归一化误差”最小化来求解最优权向量。“归一化误差”定义为接收信号的瞬时自适应无穷范数归一化的形式。与基于最小分数低阶误差波束形成算法相比，MMSNE波束形成算法计算更为简单；不需要噪声特征指数的先验信息或估计；适用于更广的冲击噪声环境；具有更小的估计误差；具有更强的干扰抑制能力。

Minimum Mean Square"Normalized-Error"Beamforming Amid Heavy-Tailed Impulsive Noise of Unknown Statistics

This paper presents a new minimum mean squared “normalized-error” (MMSNE) beamforming technique, against arbitrary unknown heavy-tailed impulsive noises. This new beamformer aims to minimize the “normalized error” between the desired signal and the the beamformer’s output. This normalized error is defined in terms of the instantaneously adaptive infinity-norm snapshot-normalized data. This new MMSNE beamformer outperforms the fractional lower order moments (FLOM)-beamformer with these advantages: (1) simpler computationally with a closed-form solution, (2) needing no prior information nor estimation of the impulsive noise’s effective characteristic exponent’s numerical value, (3) applicable to a wider class of heavy-tailed impulsive noises, (4) providing lower estimation error, and (5) offering better interference-rejection.