基于直接数据域自适应算法的相干信号DOA估计

DOA estimation of correlated signals based on the adaptive algorithm in direct data domain

利用直接数据域自适应算法的稳态权值代替噪声子空间构建空间谱,构造了一种超分辨波达方向(DOA)估计方法。为了解决谱峰搜索时的伪峰问题,采用参考阵元轮换得到多组线性无关稳态权值,逼近噪声子空间,能有效去除伪峰。针对相干信号的DOA估计,进一步提出了直接数据域取对称共轭向量的解相干方法。相比子空间分解类算法,本文算法不需估计信号源数目和协方差矩阵、不需特征分解,复杂度仅为O(MP),同时能有效完成解相干处理。

A novel method for high resolution direction-of-arrival （DOA） estimation is proposed. The novel approach constructs the spatial spectrum by utilizing the steady-state weights of the adaptive algorithm in direct data domain instead of the noise subspace. The proposed method can solve the false peak problem effectively in the spectral peak search. The noise subspace can be closed with several groups of linear non- correlated steady-state weights from utilizing different reference signals. A practical method for the DOA estimation of correlated signals is also presented, which utilizes the symmetric conjugate vector in direct data domain. Compared with the conventional method, the proposed approach does not need to estimate the source number and the covariance matrix. As it does not need the eigendecomposition and only has a computational complexity of O（ MP）, it is a practical method for DOA estimation of correlated signals.