This paper tackles the challenge of one-bit off-grid direction of arrival (DOA) estimation in a single snapshot scenario based on a learning-based Bayesian approach. Firstly, we formulate the off-grid DOA estimation model, utilizing the first-order off-grid approximation, incorporating one-bit data quantization. Subsequently, we address this problem using the Sparse Bayesian based framework and solve iteratively. However, traditional Sparse Bayesian methods often face challenges such as high computational complexity and the need for extensive hyperparameter tuning. To balance estimation accuracy and computational efficiency, we propose a novel Learning-based Sparse Bayesian framework, which leverages an unrolled neural network architecture. This framework autonomously learns hyperparameters through supervised learning, offering more accurate off-grid DOA estimates and improved computational efficiency compared to some state-of-the-art methods. Furthermore, the proposed approach is applicable to both uniform linear arrays and non-uniform sparse arrays. Simulation results validate the effectiveness of the proposed framework.

翻译：本文针对单快拍场景下的单比特离网格波达方向（DOA）估计问题，提出了一种基于学习型贝叶斯方法的解决方案。首先，我们利用一阶离网格近似并结合单比特数据量化，建立了离网格DOA估计模型。随后，我们采用基于稀疏贝叶斯的框架对该问题进行迭代求解。然而，传统的稀疏贝叶斯方法通常面临计算复杂度高、需要大量超参数调优等挑战。为了在估计精度与计算效率之间取得平衡，我们提出了一种新颖的基于学习的稀疏贝叶斯框架，该框架利用展开式神经网络架构。通过监督学习，该框架能够自主学习超参数，与一些现有先进方法相比，能够提供更精确的离网格DOA估计并提升计算效率。此外，所提方法同样适用于均匀线性阵列与非均匀稀疏阵列。仿真结果验证了所提框架的有效性。