相干分布式信源二维波达方向估计算法

针对相干分布式信源二维波达方向估计算法多采用谱峰搜索导致计算复杂度较大的问题，该文提出了一种二维波达方向分离估计算法。该算法通过将积分形式的相干分布式信源方向向量化简为点信源方向向量与实向量的Schur-Hadamard积，对子阵X接收的数据构造二阶统计量；利用传播因子最小二乘估计子阵X与Z，X与W之间的旋转不变矩阵。由二阶统计量与旋转不变矩阵分别估计方位角与仰角，对于接近90的仰角也可给出有效的估计。与传统子空间算法相比，无需任何谱峰搜索和特征值分解，降低了计算复杂度。仿真实验表明了所提算法的有效性。

2D DOA Estimation Algorithm for Coherently Distributed Source

In many two-dimensional (2D) Direction Of Arrival (DOA) estimation approaches for coherently distributed source, the computational complexity induced by 2D searching manipulation is prohibitively high. A decoupled 2D DOA estimation algorithm is proposed. The integral steering vector of coherently distributed source is deduced to be a Schur-Hadamard product comprising the steering vector of the point source and a real vector. And then a second statistics is proposed for the data collected at subarray X, the rotational invariance matrices can be estimated based on propagator method. So the azimuth and elevation angle can be obtained by the proposed second statistics and the rotational invariance matrices even if elevation angle approaches 90°. In addition, the presented method does not apply any peak-finding searching and eigenvalue decomposition, which has significantly reduced the computational complexity compared with classical subspace algorithm. Simulation results verify the effectiveness of the proposed algorithm.