基于噪声子空间特征值重构的DOA估计算法

摘要

This paper proposes an Eigenvalue Reconstruction method in Noise Subspace (ERNS) for Direction of Arrival DOA estimation with high resolution, provided that the powers of sources are different. The noise subspace eigenvalues belonging to the covariance matrix of received signals, obtained by EigenValue Decomposition (EVD), are modified to construct a new covariance matrix with respect to virtual source. The noise subspace eigenvalues corresponding to the new covariance matrix remain the same as before they are modified. The invariance of the noise subspace is utilized to estimate the DOA of emitters. The theory and process of ERNS algorithm are provided, at the same time, the theory and performance of ERNS algorithm is validated by computer simulations. The simulation results show that the ERNS algorithm has a better performance in successful probability of weak signal estimation compared with other subspace methods and MUSIC algorithm.%该文针对非等功率信号波达方向(DOA)估计问题，提出一种基于噪声子空间特征值重构(Eigenvalue Reconstruction of Noise Subspace, ERNS)的超分辨算法。算法对接收信号自相关矩阵进行特征值分解，通过重构噪声空间特征值以及引入虚拟信源来构造新的接收信号自相关矩阵，对该矩阵进行特征值分解得到新的噪声空间特征值。当虚拟信源与实际信源入射方向相同时，新噪声空间特征值与重构后噪声空间特征值保持不变，利用这一特性来估计信源入射方向。该文给出算法的原理及实现步骤，并通过仿真进行原理验证与性能分析，仿真结果表明与其他子空间算法和MUSIC 算法相比，ERNS算法能够提高弱信号估计成功的概率。