冲击噪声背景下一种稳健自适应波束形成算法

 针对冲击噪声背景下,常规波束形成算法性能下降的问题,本文提出了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基于归一化的线性约束正交投影(NLCOP)算法。该算法通过对输入信号进行无穷范数归一化,使变换信号的协方差矩阵在代数拖尾的冲击噪声环境下存在且有界,将自适应权矢量约束于噪声子空间,提高了波束形成器在冲击噪声背景下的性能。NLCOP算法无需噪声特征指数的先验信息,具有更低的副瓣电平且干扰抑制能力强。仿真结果验证了该算法的有效性。

An Algorithm of Robust Adaptive Beamforming under the Background of Heavy-tailed Impulsive Noise

In order to solve the performance degradation of conventional beamformer algorithm under the background of heavy-tailed impulsive noises, a new beamforming approach to combat the arbitrary unknown heavy-tailed impulsive noises of unknown statistics is presented. The new approach, named by Normalized Linearly Constrained Orthogonal Projection (NLCOP) algorithm, is formulated to minimize the noise power of the beamformer's output subject to a pre-specified set of linear constraints. For improving the performance of the beamformer under the background of heavy-tailed impulsive noise of unknown statistics, the new algorithm puts the weighting vector to the noise subspace after the input signal being infinity norm snapshot normalized which makes the second-order-statistics of the input signal existence and bounded. This proposed algorithm does not need prior information or estimation of the impulsive noise's effective characteristic exponent's numerical value, and offers lower sidelobe and better interference-rejection. Simulation results show the effectiveness of the proposed algorithm.