基于增广数据陈SVD的MNM 信号DOA估计算法

A Modified MNM Algorithm for Estimating DOA of Signals Based on Augmented Data Matrix in SVD

本文介绍了一种将接收数据共轭重排构造出增广数据矩阵，然后对该数据矩陈进行奇异位分解，用 ＭＮＭ算法实现对信号的 ＤＯＡ估计的方法，并指出了这将改善协方差矩阵特征值的分布，从而提高ＭＮＭ算法的角估计性能。文中最后给出了验证理论分析的模拟结果。

In this paper, an algorithm used to estimate the signal DOA with MNM (minimum-norm method) by means of the augmented data matrix in SVD (sigular value decomposition) is introduced. The augmented data matrix is constructed with received data and their conjugates. It is pointed out that this augmented data matrix would improve the eigenvalue distributions of the covariance matrix, and then make the estimation performance of signal DOA (direction of arrival) better. Finally, the simulated results confirming the theoretical analysis are pre- sented.