基于网格部分细化的DOA估计方法

计算复杂度和估计精确度一直是波达方向(DOA)估计研究的重点。现有基于压缩感知的DOA估计算法与传统算法相比具有一定优势，但这些稀疏信号重建模型都是将角度空间等间距划分，仍存在算法计算复杂度较高和估计精确度较低的问题。针对这些问题，提出一种对角度空间网格进行部分细化的DOA估计方法。该方法包括裂变过程和学习过程，裂变过程通过产生新网格点对角度空间进行细化，学习过程通过迭代不断逼近波达方向。仿真结果表明，提出的算法耗时较少，而且在非常稀疏的初始网格划分的条件下(初始间隔为20°)，仍可以获得较高的估计精确度。

Partially refined grid for DOA estimation

Direction Of Arrival(DOA) study has always been focused on the accuracy and the computational complexity. While the existing algorithms, which are based on the compressive sensing theory, have advantages over the traditional, there are still problems with high computational complexity and low estimation accuracy because the signal model is on the equal spacing grid. To solve those problems, a partially refined grid method is proposed. The proposed method consists of fission process and learning process, the fission process is to refine angle space by inserting new grid points, the learning process is constantly approaching the direction of arrival. The proposed algorithm takes less time and has higher accuracy under sparse initial grid(the initial interval is 20°).