基于子空间拟合的块稀疏贝叶斯学习DOA估计

Block sparse bayesian learning DOA estimation based on subspace fitting

针对传统基于稀疏贝叶斯学习(sparse bayesian learning,SBL)的波达方向(direction of arrival,DOA)估计算法在低信噪比条件下性能不足的问题,提出了一种基于子空间拟合和块稀疏贝叶斯学习的离网DOA估计方法。首先对样本的协方差矩阵进行特征分解,获得信号的加权子空间,然后构造等价信号的稀疏表示模型并利用块稀疏贝叶斯算法进行参数求解,同时对于网格失配带来的建模误差,将空间域内的离散采样网格点作为动态参数,通过求解一个多项式,利用期望最大化算法迭代更新离散网格点的位置。仿真实验结果表明,相对于传统SBL算法,该方法具有更好的估计精度和空间分辨率。

To improve the performance of traditional direction of arrival estimation algorithm based on sparse bayesian learning under the condition of low SNR,we propose a new off-grid DOA estimation method based on subspace fitting and block sparse Bayesian learning.Firstly,the weighted subspace of the signal is obtained by eigenvalue decomposition of the sample covariance matrix,then the sparse representation model of the equivalent signal is constructed and the parameters are solved by the block sparse Bayesian algorithm.And at the same time,for the modeling error caused by the grid mismatch,the discrete sampling grid points in the spatial domain are treated as dynamic parameters,and by solving a polynomial,the position of discrete grid points is updated iteratively using an expectation maximization algorithm.The simulation results indicate that the proposed method provides better DOA estimation accuracy and spatial resolution than the traditional SBL algorithm.