The weighting subspace fitting (WSF) algorithm performs better than the multi-signal classification (MUSIC) algorithm in the case of low signal-to-noise ratio (SNR) and when signals are correlated. In this study, we use the random matrix theory (RMT) to improve WSF. RMT focuses on the asymptotic behavior of eigenvalues and eigenvectors of random matrices with dimensions of matrices increasing at the same rate. The approximative first-order perturbation is applied in WSF when calculating statistics of the eigenvectors of sample covariance. Using the asymptotic results of the norm of the projection from the sample covariance matrix signal subspace onto the real signal in the random matrix theory, the method of calculating WSF is obtained. Numerical results are shown to prove the superiority of RMT in scenarios with few snapshots and a low SNR.

在低信噪比（SNR）以及信号相关的情况下，加权子空间拟合（WSF）算法的性能优于多信号分类（MUSIC）算法。在这项研究中，我们使用随机矩阵理论（RMT）来改善WSF。RMT专注于随机矩阵的特征值和特征向量的渐近行为，矩阵的维数以相同的速率增加。在计算样本协方差特征向量的统计量时，在WSF中应用近似一阶微扰。在随机矩阵理论中，使用样本协方差矩阵信号子空间到实信号的投影范数的渐近结果，获得了WSF的计算方法。数值结果表明，在快照少，信噪比低的情况下，RMT具有优越性。