基于特征矢量稀疏分解的DOA估计方法
A novel DOA estimation approach based on sparse decomposition of eigenvector
李鹏飞 1张旻 2钟子发2
作者信息
- 1. 解放军63880部队,河南洛阳471003
- 2. 合肥电子工程学院,安徽合肥230037
- 折叠
摘要
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.
Abstract
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.
关键词
波达方向/稀疏分解/特征值分解/特征矢量Key words
direction of arrival/sparse decomposition/eigenvalue decomposition/eigenvector引用本文复制引用
出版年
2013
NETL
NSTL
维普
万方数据