中文摘要：

针对采用l 1范数优化的稀疏表示DOA估计算法正则化参数选取困难、计算复杂度高的问题,该文提出一种基于稀疏贝叶斯学习的高效算法。该算法首先利用均匀线阵的结构特性,将DOA估计联合稀疏模型的构建与求解转换到实数域进行。其次,通过优化稀疏贝叶斯学习的基消除机制,使该算法具有更快的收敛速度。仿真结果表明,与l 1范数优化类算法相比,该文方法具有更高的空间分辨率和估计精度且计算复杂度低。

英文摘要：

Sparsity-based Direction-Of-Arrival（DOA） estimation via l 1-norm optimization requires fine tuning of the regularization parameter and large computational times.To alleviate these problems,this paper presents an efficient approach based on Sparse Bayesian Learning（SBL）.The presented approach constructs and solves the jointly sparse DOA estimation model in real domain by making good use of the special geometry of the uniform linear array.Furthermore,the basis pruning mechanism of sparse Bayesian learning is modified to speed up the convergence rate.Simulation results demonstrate that the presented approach provides higher spatial resolution and accuracy with lower computational complexity in comparison with those l 1-norm-based estimators.