In this paper, we focus on estimating Direction of Arrival (DOA) and removing heavy clutter embedded with measurement noise. A correlated Gaussian process is chosen to model destructive effects of clutter. Also, a white Gaussian process is selected to describe measurement noise caused by sensor array. After adding these distortions to the off-grid model, we utilize Sparse Bayesian Learning and principal component analysis (as a preprocessing stage) in order to remove these distortions as well as estimating of true DOAs. Finally, at the end we will show how ignorance of clutter from model or combine it with measurement noise degrade DOA estimation. This will be demonstrated by various numerical simulations.

中文翻译：

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存在测量噪声和重杂乱环境下的离网到达方向估计

在本文中，我们专注于估计到达方向（DOA）并去除嵌入测量噪声的重杂波。选择相关的高斯过程来模拟杂波的破坏性影响。此外，选择白高斯过程来描述由传感器阵列引起的测量噪声。在将这些失真添加到离网模型后，我们利用稀疏贝叶斯学习和主成分分析（作为预处理阶段）来消除这些失真并估计真实的 DOA。最后，在最后，我们将展示对来自模型的杂波的无知或将其与测量噪声相结合如何降低 DOA 估计。这将通过各种数值模拟来证明。