基于互质阵列的协方差矩阵重构算法

Covariance Matrix Reconstruction Algorithm Based on Coprime Arrays

针对基于互质阵列波达方向(direction of arrival, DOA)估计方法对连续虚拟阵元得到的样本协方差矩阵信息利用率不高的问题,提出一种基于互质阵列的协方差矩阵重构算法。该算法利用最大连续虚拟均匀线阵协方差矩阵的每一行元素进行Toeplitz矩阵重构,再对这些矩阵加权求和获得新的满秩协方差矩阵,提高对接收数据的利用率并消除噪声贡献对DOA估计结果的影响。理论分析和仿真结果表明,该算法能实现欠定DOA估计,在低信噪比、小快拍数、入射角度间隔小条件下有良好的角度估计精度。

A covariance matrix reconstruction algorithm based on coprime arrays is proposed to address the low utilization of sample covariance matrix information obtained from continuous virtual elements using the direction of arrival(DOA)estimation based on coprime arrays.This algorithm uses each row element of the maximum continuous virtual uniform linear array covariance matrix to reconstruct the Toeplitz matrix,and weights and sums these matrices to obtain a new full rank covariance matrix,thus improving the utilization of received data and eliminating the impact of noise contribution on DOA estimation results.Theoretical analysis and simulation results show that the algorithm can achieve underdetermined DOA estimation and has good angle estimation accuracy under low signal-to-noise ratio,small number of snapshots,and small incident angle interval.