标题： 基于深度学习的概率贝叶斯神经网络的常规互质阵列到达方向估计 显示英文标题

标题： Direction-of-arrival estimation with conventional co-prime arrays using deep learning-based probablistic Bayesian neural networks

摘要： 本文研究了利用概率贝叶斯神经网络（PBNN）对窄带信号的到达方向（DOA）估计，使用传统的共质阵列。一种基于贝叶斯神经网络和空间超完备阵列输出公式的超分辨率DOA估计方法克服了模型驱动DOA估计方法的预假设依赖性。所提出的DOA估计方法利用PBNN模型来捕捉数据和模型的不确定性。开发的PBNN模型被训练以执行从伪谱到超分辨率谱的映射。这种基于学习的方法增强了未训练场景的泛化能力，并提供了对非理想条件的鲁棒性，例如小角度分离、数据稀缺和不完善阵列等。仿真结果展示了PBNN模型和确定性模型的损失曲线。进行了仿真以验证PBNN模型相对于传统神经网络（CNN）确定性模型的性能。 显示英文摘要

摘要： The paper investigates the direction-of-arrival (DOA) estimation of narrow band signals with conventional co-prime arrays by using probabilistic Bayesian neural networks (PBNN). A super resolution DOA estimation method based on Bayesian neural networks and a spatially overcomplete array output formulation overcomes the pre-assumption dependencies of the model-driven DOA estimation methods. The proposed DOA estimation method utilizes a PBNN model to capture both data and model uncertainty. The developed PBNN model is trained to do the mapping from the pseudo-spectrum to the super resolution spectrum. This learning-based method enhances the generalization of untrained scenarios, and it provides robustness to non-ideal conditions, e.g., small angle separation, data scarcity, and imperfect arrays, etc. Simulation results demonstrate the loss curves of the PBNN model and deterministic model. Simulations are carried out to validate the performance of PBNN model compared to a deterministic model of conventional neural networks (CNN).