BeamFocusNet：基于波束形成的显式空间信号聚焦用于低信噪比和单快照条件下的稳健DoA估计
BeamFocusNet: Beamforming-based Explicit Spatial Signal Focusing for Robust DoA Estimation under Low-SNR and Single-Snapshot Conditions
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中文总结 AI 辅助
针对低信噪比和单快照下DoA估计鲁棒性差的问题，提出BeamFocusNet，通过显式信号聚焦生成滤波器，实验证明其优越性和鲁棒性。
中文摘要 AI 辅助
DoA估计在信号处理中起着至关重要的作用。受波束形成的启发，近期工作采用神经网络来估计滤波器，以对接收信号进行滤波从而实现DoA估计。这些方法通常通过隐式聚焦信号和抑制噪声来生成滤波器。然而，这耦合了两个目标，使得训练难以平衡，并导致鲁棒性差，尤其是在低信噪比和有限快照条件下。为解决此问题，我们提出了BeamFocusNet方法，该方法通过显式信号聚焦生成滤波器，为基于神经网络的滤波器生成引入了新范式。在各种挑战性条件下的广泛实验证明了所提方法在DoA估计中的优越性和鲁棒性。代码可在该https URL获取。
英文摘要
DoA estimation plays a crucial role in signal processing. Inspired by beamforming, recent works employ neural networks to estimate filters for filtering received signals to achieve DoA estimation. These methods typically generate filters by implicitly focusing signals and suppressing noise. However, this couples the two objectives, making training difficult to balance and resulting in poor robustness, especially under low SNR and limited snapshots. To address this issue, we propose the BeamFocusNet method, which generates filters through explicit signal focusing, introducing a new paradigm for neural network-based filter generation. Extensive experiments under various challenging conditions demonstrate the superiority and robustness of the proposed method in DoA estimation. Code is available at https://github.com/colaudiolab/BeamFocusNet.
