Sample Matrix Inversion (SMI) algorithm is one part of adaptive algorithm. Because it requires the direction of arrival of signals, it absolutely belongs to non-blind algorithms. Two improvements of SMI algorithm are presented based on the original one in this paper. The method is proposed that the covariance matrix is decomposed into signal subspaces and then weighted, thereby the main-lobe gain of the desired signal and the signal to interference and noise ratio of output are improved. Meanwhile, since the traditional SMI algorithm can not be applied when the input signals are correlated, a decorrelating approach is used to solve the problem.%采样矩阵求逆(SMI)算法是自适应算法中的一个分类.由于该算法需要知道接收信号的波达方向角度,因此属于非盲算法.在现有采样矩阵求逆算法的基础上进行了2项改进,提出了对协方差矩阵进行信号子空间分解再加权的方法,有效改善了输出期望信号的主辩增益和输出信干噪比;同时针对传统SMI算法无法应用于相干信号源的情况做了进一步改进,通过对相干信号进行解相关,解决了这一问题.
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