基于实数化的均匀圆阵矩阵重构方法

A matrix reconstruction method based on real numbers and uniform circular array

为了减小低快拍数和低信噪比下采样协方差矩阵误差,并降低其运算复杂度,提出了一种基于实数化的均匀圆阵采样协方差矩阵重构方法。针对均匀圆阵的特点,通过组建特殊的基向量,构成特殊的重构矩阵。通过将采样协方差矩阵实数化,进一步降低了重构矩阵的复杂度。考虑到多通道不一致性对重构矩阵的影响,引入0位校正算法,提高了重构方法的稳健性。最后应用重构后的协方差矩阵进行子空间类波达方向估计(direction of arrival,DOA)。实验仿真证明,该特殊重构矩阵在实数化下与原矩阵重构能力相同;当快拍数为100、信噪比为0 dB时,双信源分辨力较重构前由74%提高到95%以上;理论重构运算复杂度降低到原来的53.99%。

In order to reduce the covariance matrix error and computational complexity of downsampling with low number of snapshots and low signal-to-noise ratio,a real number based uniform circular array sampling covariance matrix reconstruction method is proposed.Based on the characteristics of uniform circular arrays,a special reconstruction matrix is constructed by constructing special basis vectors.By quantifying the sampling covariance matrix,the complexity of the reconstruction matrix is further reduced.Considering the impact of multi-channel inconsistency on the reconstruction matrix,a zero bit correction algorithm is introduced to improve the robustness of the reconstruction method.Finally,the reconstructed covariance matrix is applied to the subspace class direction of arrival(DOA)estimation.Experimental simulation shows that this special reconstruction matrix has the same reconstruction ability as the original matrix in real number.When the number of snapshots was 100 and the signal-to-noise ratio was 0dB,the resolution of dual sources was improved from 74%to over 95%compared with that before reconstruction;The complexity of theoretical reconstruction operation was reduced to 53.99%of the original one.