混合信源波达方向估计算法

本文针对非相干混合点信源和分布式信源，提出了一种基于对称均匀线阵的波达方向估计算法。该算法利用点信源和分布式信源协方差矩阵结构的不同，采用空间差分技术将两种信源分离。对于点信源，采用传统MUSIC算法估计其波达方向；对于分布式信源，利用信号子空间的旋转不变性来估计其波达方向。该算法不仅消除了点信源对分布式信源的影响，也无需估计分布参数，大大降低了计算复杂度。且采用2N+1个阵元的对称均匀线阵可估计出 2N 个混合信源，其中分布式信源最多为 N 个，有效减小了阵列的孔径损失。仿真结果表明该算法的性能优于广义特征值分解的算法。 

Direction-of-Arrival Estimation for Mixed Sources

In this paper, based on symmetric uniform linear array (ULA), a direction-of-arrival (DOA) estimation algorithm was proposed for incoherently mixed point sources and distributed sources. The spatial differencing technique was adopted to separate the two kinds of sources by exploiting the difference of covariance matrix structure between point sources and distributed sources. For the point sources, the traditional MUSIC algorithm was used to estimate the DOA. For the distributed ones, the DOA was estimated by exploiting the rotational invariance of the signal subspace. Our algorithm not only eliminated the effects of the point sources on the distributed ones, but also did not need to estimate the distribution parameters, which decreased the computational complexity greatly. With a symmetric ULA of 2N+1 elements, 2N mixed sources can be estimated, including N distributed sources at most, which effectively decreases the array aperture loss. Simulation results show that the proposed algorithm outperforms the generalized eigenvalue decomposition (GEVD) algorithm.