The angle of Arrival (AoA) estimation is of great interest in modern communication systems. Traditional maximum likelihood-based iterative algorithms are sensitive to initialization and cannot be used online. We propose a Bayesian method to find AoA that is insensitive towards initialization. The proposed method is less complex and needs fewer computing resources than traditional deep learning-based methods. It has a faster convergence than the brute-force methods. Further, a Hedge type solution is proposed that helps to deploy the method online to handle the situations where the channel noise and antenna configuration in the receiver change over time. The proposed method achieves $92\%$ accuracy in a channel of noise variance $10^{-6}$ with $19.3\%$ of the brute-force method's computation.

翻译：Arriveval(AoA)估算角度对现代通信系统极感兴趣,传统的基于可能性的极有可能迭代算法对初始化十分敏感,无法在线使用。我们建议采用一种对初始化不敏感的巴伊西亚方法寻找AoA。拟议方法不那么复杂,比传统的深层次学习方法需要更少的计算资源。其趋同速度快于粗力方法。此外,还提议采用一种尖端型解决办法,帮助在网上部署方法,处理接收器中频道噪音和天线配置随时间变化的情况。拟议方法在噪音差异10 ⁇ -6 美元、布鲁特方法计算19.3 美元的情况下实现了92 $的准确度。