MUSIC (MUltiple SIgnal Characterization) is a special spectral estimation method based on the eigen decomposition of the sample covariance matrix.

多重信号分类(MUSIC)算法是通过对数据协方差矩阵进行本征分解获得信号空间谱估计的方法。

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A spectral estimator based on the rank-deficient sample covariance matrix was developed to improve the robustness of estimates of the rank-deficient robust Capon filter-bank (RCF) spectral estimator.

为了解决秩亏RCF（robust Capon filter-bank）谱 估计 方法的 估计性能不稳健问题，提出一种 基于奇异协方差矩阵的谱 估计 方法。

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This approach, unlike the conventional statistical techniques requiring for a covariance matrix of sample, is based on direct spatial processing of the array data.

这种方法不同于传统的统计方法需要计算样本协方差矩阵的逆矩阵，而是基于阵列数据的一种直接计算方法。

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