基于子空间模型的稀疏贝叶斯DOA估计  

Sparse Bayesian DOA Estimation Based on Subspace Model

作　　者：窦慧晶 郭宏亮 邢路阳 路瑶 DOU Huijing;GUO Hongliang;XING Luyang;LU Yao(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学信息学部,北京100124

出　　处：《北京工业大学学报》2024年第12期1421-1427,共7页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(61171137);北京市教育委员会科技计划资助项目(KM201210005001)。

摘　　要：为了提高相干信源条件下的离格波达方向(direction of arrival,DOA)估计精度,提出一种基于子空间模型的稀疏贝叶斯学习(sparse Bayesian learning,SBL)的DOA估计算法。该算法首先将子空间平滑(subspace smoothing,SS)技术与加权子空间拟合(weighted subspace fitting,WSF)技术结合,然后将此子空间模型应用于SBL算法,并将离散网格点视为动态参数,用期望最大化(expectation maximization,EM)算法迭代更新网格点位置。与传统稀疏恢复算法相比,该算法在估计误差及计算复杂度上均具有明显优势,并对信源数目的估计误差具有较强的鲁棒性。A direction of arrival(DOA)estimation algorithm based on a subspace model using sparse Bayesian learning(SBL)is proposed to improve the accuracy of the DOA estimation under coherent source conditions.First,the subspace smoothing(SS)technique was integrated with the weighted subspace fitting technique.This subspace model was then utilized in the SBL algorithm,the discrete grid points were considered as dynamic parameters and the grid point positions were updated iteratively using the expectation-maximization(EM)algorithm.Compared with traditional sparse algorithms,the proposed algorithm has significant advantages in terms of root mean square error and computational complexity,and has strong robustness to the estimation error of the number of signal sources.

关 键 词：阵列信号处理 波达方向(direction of arrival DOA)估计 压缩感知 稀疏贝叶斯学习(sparse Bayesian learning SBL) 子空间 稀疏恢复 

分 类 号：TN911[电子电信—通信与信息系统]