基于特征矢量稀疏分解的DOA估计方法

摘要

The thesis proposes a novel DOA (direction of arrival) estimation method using sparse decomposition of eigenvector on the basic of the sparse characteristic of space signals.Firstly,the biggest eigenvector of covariance matrix is proved to be the linear combination of all steer vectors.Then the biggest eigenvector of covariance matrix is extracted to build sparse decomposition model for DOA estimation,the effects caused by the noise is largely reduced and the sources number estimation is able to skip by this method.The theoretical analysis and experimental results show this new method has a better performance than the MUSIC algorithm in the aspects of accuracy,resolution and adaptability to coherent signals.%论文提出了一种基于特征向量稀疏分解的DOA估计方法.依据阵列协方差矩阵的最大特征向量是所有信号导向矢量的线性组合这一性质.利用阵列协方差矩阵的最大特征向量建立稀疏模型进行DOA估计.该方法能有效降低噪声的影响,避免估计信号源数目,增强了算法的鲁棒性.理论分析和仿真实验,验证了本文方法具有较高的精度、较好的分辨力、对相干信号也具有优越的适应能力,性能优于MUSIC算法.