In this letter, we propose two angle separation learning schemes (ASLs) to address the coherent DOA estimation problem. We first show that the columns of the array covariance matrix can be formulated as under-sampled linear measurements of the spatial spectrum. Secondly, the computational load of coherent DOA estimation contains two-dimensional searching process, which is reduced through introducing angle separation. Correspondingly, two classification learning models integrating the angle separation are proposed to solve the problem of coherent DOA estimation. Compared to classic sparsity-inducing methods with complex computational interactions, the proposed ASLs can efficiently obtain DOA estimates in near real time. Moreover, the proposed ASLs improve the DOA estimation performance compared to existing deep-learning based methods. Numerical experiments prove the effectiveness and superiority of the presented ASLs. Simulation results show that the new techniques have a better performance in term of estimation errors and generalization ability than the classic physics-driven and state-of-the-art deep-learning based DOA estimation methods, especially in demanding scenarios with low SNR and limited snapshots.

中文翻译：

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深稀疏先验相干DOA估计的角度分离学习

在这封信中，我们提出了两种角度分离学习方案（ASL），以解决相干DOA估计问题。我们首先表明，阵列协方差矩阵的列可以表示为空间谱的欠采样线性度量。其次，相干DOA估计的计算量包含二维搜索过程，该过程通过引入角度分离来减少。相应地，提出了两种结合角度分离的分类学习模型，以解决相干DOA估计问题。与具有复杂计算交互作用的经典稀疏诱导方法相比，所提出的ASL可以近实时地高效获得DOA估计。此外，与现有的基于深度学习的方法相比，提出的ASL改善了DOA估计性能。数值实验证明了所提出的ASL的有效性和优越性。仿真结果表明，新技术在估计误差和泛化能力方面比经典的基于物理驱动和最新的深度学习的DOA估计方法具有更好的性能，尤其是在信噪比低且要求严格的场景中快照。