In this paper, the direction of arrival (DOA) estimation of signals in the presence of impulsive noise environment is studied. Complex isotropic symmetric alpha-stable (SαS) random variables are modeled as impulsive noise, then a novel second-order statistic method that correntropy-based covariance matrix (CBCM) is defined, based on the combination of the CBCM of the array sensor outputs with the signal subspace technique (e.g., multiple signal classification (MUSIC)), which can be achieved source localization under impulsive noise environments. The Monte-Carlo simulation results illustrate the improved performance of CBCM-MUSIC for DOA estimation under a wide range of impulsive noise conditions.

本文研究了脉冲噪声环境下信号的到达方向（DOA）估计。将复杂的各向同性对称α稳定（SαS）随机变量建模为脉冲噪声，然后基于阵列传感器输出的CBCM与C的组合，定义了一种新的基于统计量的协方差矩阵（CBCM）的二阶统计方法。信号子空间技术（例如，多信号分类（MUSIC）），可以在脉冲噪声环境下实现源定位。蒙特卡洛仿真结果表明，在大范围的脉冲噪声条件下，CBCM-MUSIC用于DOA估计的性能有所提高。