摘要
本文提出了基于阵列协方差矩阵稀疏表示的DOA估计方法。该方法首先对接收数据的统计协方差矩阵向量化,并进行重排序和去冗余,得到虚拟阵列的入射信息;然后对整个角度空间域进行栅格划分,将DOA估计问题转化为稀疏表示模型,并利用模型的平方误差分布特性确定正则化参数;最后利用数值软件包,通过内点法求解。仿真结果表明,该方法计算量较低,并且在低信噪比、低快拍数下具有良好的性能。
This paper proposes a DOA estimation method based on the sparse representation of array covariance matrix.The method firstly vectorizes the statistical covariance matrix of the received data, and reorders and de-redundancy to obtain the incident information of the virtual array;then divides the entire angle space domain into a grid, and converts the DOA estimation problem into a sparse representation model, and use the square error distribution characteristic of the model to determine the regularization parameter;finally, the numerical software package is used to solve the problem by interior point method.The simulation results show that the method has good performance with low computational complexity, low signal-to-noise ratio and low number of snapshots.
作者
苏龙
谷绍湖
SU Long;GU Shaohu
出处
《计量与测试技术》
2022年第12期7-10,共4页
Metrology & Measurement Technique
基金
湖南省教育厅科学研究项目一般项目(项目编号:19C0017)。
关键词
阵列协方差矩阵
稀疏表示
虚拟阵列
正则化参数
array covariance matrix
sparse representation
virtual array
regularization parameter
