In this letter, we proposed a novel unsupervised learning strategy for direction-of-arrival (DOA) estimation network. Inspired by the sparse power spectrum and $\ell _1$ -norm optimization, we develop a novel loss function to cooperate with the estimation network. Unlike the prior DL-based methods, the proposed method does not need any manual annotations for training and validation datasets. Compared with state-of-art methods, the proposed method can automatically increase the degree of freedom of the array without further pre-processing on the covariance matrix of array observation data. Moreover, the proposed method can obtain clear spectrum and precise DOAs under harsh estimation environments.

中文翻译：

---

到达方向估计网络的无监督学习策略


在这封信中，我们提出了一种新颖的用于到达方向（DOA）估计网络的无监督学习策略。受稀疏功率谱和 $\ell _1$ 范数优化的启发，我们开发了一种新颖的损失函数来与估计网络配合。与之前基于深度学习的方法不同，所提出的方法不需要对训练和验证数据集进行任何手动注释。与最先进的方法相比，该方法可以自动增加阵列的自由度，而无需对阵列观测数据的协方差矩阵进行进一步的预处理。此外，该方法可以在恶劣的估计环境下获得清晰的频谱和精确的DOA。