基于酉重构子空间的一维DOA估计

One-Dimensional DOA Estimation Based on Unitary Reconstructive Subspace

针对多重信号分类(multiple signal classification, MUSIC)算法在低阵元数目、低信噪比和小节拍数等非理想条件下,对入射间隔较小的信号波达方向(direction of arrival,DOA)估计有效性的问题,提出了改进的基于酉重构子空间的MUSIC算法。该算法首先利用酉变换将均匀线阵接收数据实数化,然后根据子空间特征向量的大小,重新构造子空间和校正矩阵得到新的空间谱函数,最后与信号子空间投影算法联合,实现DOA估计。仿真结果表明,与传统MUSIC算法和SSP算法相比,所提算法在低阵元数目、低信噪比和小节拍条件下具有更好的分辨率。

To address the limitations of traditional multiple signal classification(MUSIC)algorithm,such as ineffective performance in low signal to noise ratio(SNR),small snapshots and low array number under small incident angle interval signals,we propose an improved algorithm called unitary reconstructed subspace MUSIC(URS-MUSIC).The proposed algorithm transforms the actual received signal of a uniform linear array from complex to real value using unitary transformation,then reconstructs subspaces and revised matrices to obtain new spatial spectrums based on the size of the subspace eigenvectors.The obtained spectrums are multiplied by the signal subspace projection(SSP)to realize direction of arrival(DOA)estimation.Simulation results demonstrate that URS-MUSIC outperforms both traditional and signal subspace projection algorithms with better resolution performance,especially under challenging conditions such as low SNR,small snapshots,and low array numbers.