基于稀疏贝叶斯学习在未知噪声场的欠定宽带信号DOA估计

DOA Estimation of Underdetermined Wideband Signal Based on Sparse Bayesian Learning in Unknown Noise Field

稀疏贝叶斯学习(SBL)应用于互质阵列、欠定宽带信号在未知噪声场的波达方向(DOA)估计。使用扩展协方差矩阵,互质阵列可以实现更高数量的自由度(DOF),以解析比物理传感器的数量更多的源。基于稀疏的DOA估计可能恶化检测和估计性能,因为无论网格有多精细,源都可能离开搜索网格。SBL使用定点更新可以较好地解决这种字典不匹配问题。与同时正交匹配跟踪最小二乘(SOMP-LS)、同时正交匹配跟踪总最小二乘(SOMP-TLS)和离网稀疏贝叶斯推理(OGSBI)相比,SBL能够获得较好的检测和估计性能。

Sparse Bayesian learning(SBL)is applied to the estimation of DOA of underdetermined wideband signals in unknown noise field.Using the augmented covariance matrix,the coprime array can achieve a higher number of degrees of freedom(DOF)to resolve more sources than the number of physical sensors.The sparse-based DOA estimation can deteriorate the detection and estimation performance because the sources may be out of the search grid no matter how fine the grid is.This dictionary mismatch problem can be well resolved by the SBL using fixed point updates.It is clear that SBL can obtain good performance in detection and estimation comparing with simultaneous orthogonal matching pursuit least squares(SOMP-LS),simultaneous orthogonal matching pursuit total least squares(SOMP-TLS)and off-grid sparse Bayesian inference(OGSBI).