基于改进块稀疏贝叶斯学习算法的波达方向估计

针对传统的基于稀疏表示的DOA估计算法单纯利用信号的空域稀疏性，导致在低信噪比时稀疏性能变差，影响信号稀疏重构效果的问题，使用分块稀疏理论对信号进行稀疏分解。随着目标增多及作战任务改变，DOA估计往往呈现目标群测向的特点，为了能够更好地利用信号的结构特征和统计特征，提出了基于空时联合的块稀疏DOA估计算法，使用块稀疏理论挖掘信号的内部结构，充分利用了信号的块内稀疏性和块间相关性，提高稀疏重构性能，进而对DOA估计效果有很大的提升。仿真实验表明，相比于经典的DOA方法，本方法有更好的估计效果。

DOA estimation based on improved block sparse Bayesian learning algorithm

The traditional DOA estimation algorithm based on sparse representation only uses sparse signal in spatial domain, which leads to poor sparse performance at low SNR and affects the effect of sparse signal reconstruction. This paper used block sparse theory to decompose the signal sparsely. With the increase of target and the change of combat mission, DOA estimation often presents the characteristics of target group direction finding. In order to make better use of the structural and statistical characteristics of signals, this paper proposed a block sparse DOA estimation algorithm based on space-time combination(STC-BSBL). This method improves the performance of sparse reconstruction, and thus greatly improves the DOA estimation effect. Simulation results show that the proposed method has better estimation performance compared with the traditional DOA method.