基于贝叶斯压缩感知的子空间拟合离格DOA估计

针对传统的基于稀疏贝叶斯学习（Sparse Bayesian Learning,SBL）的波达方向估计算法对噪声鲁棒性不高的问题，提出了一种基于SBL的子空间拟合离格波达方向(Direction of Arrival,DOA)估计方法。首先对接收数据的协方差矩阵进行特征分解，获得信号的加权子空间，构造等价信号的稀疏表示模型并利用贝叶斯学习算法进行参数求解。同时对于网格划分带来的建模误差问题，采用了离格贝叶斯推导（Sparse Bayesian Inference,SBI）算法进行求解，利用期望最大化算法迭代更新相应的参数。仿真结果表明，相对于传统的DOA方法，该方法具有更好的估计精度。

Subspace fitting off-grid DOA estimation based on Bayesian compressed sensing

For the problem that the traditional direction of arrival(DOA) estimation algorithm based on sparse Bayesian learning(SBL) is not robust to noise,an off-grid DOA estimation method based on SBL and subspace fitting is proposed.First,the covariance matrix of the received data is decomposed by feature decomposition to obtain the weighted subspace of the signal.Then,the sparse representation model of the equivalent signal is constructed.And the parameters are solved by Bayesian learning algorithm.At the same time,the off-grid Bayesian inference algorithm is used to solve the modeling error problem caused by grid division.The expectation maximization algorithm is used to iteratively update the corresponding parameters.The simulation results show that this method has better estimation accuracy than the traditional DOA method.