In this paper, we develop a robust algorithm for improving the accuracy of direction-of-arrival estimation under non-Gaussian noise and insufficient sample support. (The number of sensors is large, while the number of samples is relatively small.) Unlike the traditional peak-search techniques, our approach is based on an enhanced covariance matrix estimation, where we exploit the thoughts of the M-estimator and the shrinkage estimator, but devise a new target matrix equation and iterative solution procedure. Numerical results indicate that the proposed algorithm significantly performs better than the existing methods in the presence of non-Gaussian noise and finite samples.

中文翻译：

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非高斯噪声和样本支持不足下的鲁棒 DOA 估计器

在本文中，我们开发了一种鲁棒算法，用于在非高斯噪声和样本支持不足的情况下提高到达方向估计的准确性。（传感器的数量很多，而样本的数量相对较少。）与传统的峰值搜索技术不同，我们的方法基于增强的协方差矩阵估计，我们利用了 M-estimator 和收缩的思想estimator，但设计一个新的目标矩阵方程和迭代求解程序。数值结果表明，在存在非高斯噪声和有限样本的情况下，所提出的算法明显优于现有方法。