In this work, we consider direction-of-arrival (DoA) estimation in the presence of extreme noise using Deep Learning (DL). In particular, we introduce a Convolutional Neural Network (CNN) that predicts angular directions using the sample covariance matrix estimate. The network is trained from multi-channel data of the true array manifold matrix in the low signal-to-noise-ratio (SNR) regime. By adopting an on-grid approach, we model the problem as a multi-label classification task and train the CNN to predict DoAs across all SNRs. The proposed architecture demonstrates enhanced robustness in the presence of noise, and resilience to a relatively small number of snapshots. Moreover, it is able to resolve angles within the grid resolution. Experimental results demonstrate significant performance gains in the low-SNR regime compared to state-of-the-art methods and without the requirement of any parameter tuning in both cases of correlated and uncorrelated sources. Finally, we relax the assumption that the number of sources is known a priori and present a training method, where the CNN learns to infer their number and predict the DoAs with high confidence. The increased robustness of the proposed solution is highly desirable in challenging scenarios that arise in several fields, ranging from wireless array sensors to acoustic microphones or sonars.

中文翻译：

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用于低信噪比下到达方向估计的深度网络

在这项工作中，我们使用深度学习 (DL) 在存在极端噪声的情况下考虑到达方向 (DoA) 估计。特别是，我们引入了一个卷积神经网络 (CNN)，它使用样本协方差矩阵估计来预测角度方向。该网络是从低信噪比 (SNR) 状态下真实阵列流形矩阵的多通道数据中训练的。通过采用网格方法，我们将问题建模为多标签分类任务，并训练 CNN 预测所有 SNR 的 DoA。所提出的架构展示了在存在噪声的情况下增强的鲁棒性，以及对相对少量快照的弹性。此外，它能够在网格分辨率内解析角度。实验结果表明，与最先进的方法相比，在低 SNR 状态下的性能显着提高，并且在相关和不相关源的两种情况下都不需要任何参数调整。最后，我们放宽了源数量已知的假设先验并提出一种训练方法，其中 CNN 学习推断它们的数量并以高置信度预测 DoA。在从无线阵列传感器到声学麦克风或声纳等多个领域中出现的具有挑战性的场景中，非常希望所提出的解决方案具有更高的鲁棒性。