采用信号子空间稀疏表示的DOA估计方法

利用目标辐射源空间分布的稀疏性,提出了一种基于稀疏表示的多快拍联合波达方向(direction of arrival, DOA)估计方法。该方法首先利用采样数据矩阵大奇异值对应的左奇异向量估计信号子空间,然后采用加权迭代最小方差方法对信号空间进行稀疏表示。与传统的角度高分辨估计方法不同,该方法没有利用样本的统计信息,因而对具有任意相关性的信号源能进行有效的波达方向估计,不需要进行去相关处理,且具有很高的分辨力及估计精度。实验表明在该方法能准确的对目标源方位进行估计,且极大地降低了稀疏表示的计算量。

DOA estimation method using sparse representation of the signal subspace

Using the spatial sparsity of the source signals, a direction of arrival (DOA) estimation method is proposed based on the sparse representation of multiple measurement vectors (MMV). First, the signal subspace is estimated by taking the left singular eigenvectors of the samples matrix corresponding to the big singular eigenvalues. Then the new re-weighted iterative minimum variance (RIMV) method is adopted to the signal subspace to determine the DOA of the targets. In addition, since it does not utilize the statistic information like the conventional high-resolution methods do, the proposed method can effectively distinguish the signal sources with any coherence without decorrelation processing. Experimental results show that the method can exactly estimate the DOA of source signals and bring a great computation reduction.