基于云平台的铁路无线电信号DOA跟踪算法

DOA tracking algorithm for railway wireless radio signals based on cloud platform

在高速铁路场景下,准确估计和跟踪无线电信号的波达方向(Direction of Arrival, DOA)能够有效提升无线通信服务质量.然而,高速移动的无线信道具有快速时变特性,对信号处理的速度和准确性提出了更高的挑战.针对传统的基于信号子空间的DOA估计算法,由于巨大的计算量而无法应用于高速铁路快速时变系统中进行DOA跟踪的问题,提出了基于卡尔曼滤波和正交压缩近似投影子空间跟踪(Kalman Filter-Orthonormal Projection Approximation and Subspace Tracking of deflation, K-OPASTd)的DOA算法.首先,搭建基于云平台的铁路信号动态测向系统;然后,建立列车接收信号模型,提出K-OPASTd算法对DOA进行动态跟踪;最后,将本文提出的算法与OPASTd算法所得到的估计角度的均方根误差进行仿真对比实验.研究结果表明:信噪比均为10dB时,本文所提算法的均方根误差比OPASTd算法低约60%;阵元均为20时,K-OPASTd算法的均方根误差比OPASTd算法低约80%.

In high-speed railway scenarios,accurate estimation and tracking of the Direction of Arrival(DOA)of radio signals can significantly enhance the quality of wireless communication services.How-ever,the rapidly changing radio channels in high-speed railway environments present unique challenges in terms of speed and accuracy for signal processing.Traditional DOA estimation algorithms based on signal subspaces are not suitable for DOA tracking in fast time-varying systems due to their computa-tional complexity.To solve this problem,in this paper,a DOA tracking algorithm,named Kalman Filter-Orthonormal Projection Approximation and Subspace Tracking of deflation(K-OPASTd),is pro-posed.Firstly,a dynamic direction finding system for railway signals is established on a cloud platform.Then,a model for the signals received by trains is developed,and the K-OPASTd algorithm is intro-duced for dynamic DOA tracking.Finally,the root-mean-square errors of the estimated angles obtained by the algorithm proposed in this paper are compared with the OPASTd algorithm.The results demon-strate that,at a signal-to-noise ratio of 10 dB,the proposed algorithm achieves a 60%reduction in root-mean-square error compared to the OPASTd algorithm;with an array size of 20 elements,the proposed algorithm reduces the root-mean-square error by approximately 80%.