Various methods have been proposed to estimate the direction of arrival (DOA) of sources under the assumption of Gaussian noise. This assumption, based on the central limit theorem, has been mainly used because it offers an appropriate model in a homogeneous environment. Nevertheless, under certain conditions, the Gaussian hypothesis cannot fully represent the noisy environment. Consequently, these classical estimation methods are no longer suitable, and the use of non-Gaussian noise model is necessary. In this paper, we therefore treat the DOA estimation problem in a non-Gaussian framework based on the maximum likelihood approach using a compound-Gaussian (CG) noise model. We then propose the use of the expectation maximization (EM) algorithm to reduce the computational cost of the proposed algorithm. Several simulations are carried out to illustrate the interest of our approach compared to the state of the art.

中文翻译：

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非高斯噪声下的二维鲁棒源定位

已经提出了各种方法来在高斯噪声假设下估计源的到达方向（DOA）。这个基于中心极限定理的假设主要被使用，因为它在齐次环境中提供了一个合适的模型。然而，在某些条件下，高斯假设不能完全代表嘈杂的环境。因此，这些经典的估计方法不再适用，需要使用非高斯噪声模型。因此，在本文中，我们基于最大似然方法使用复合高斯 (CG) 噪声模型在非高斯框架中处理 DOA 估计问题。然后我们提出使用期望最大化（EM）算法来降低所提出算法的计算成本。