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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However, there have been few outcomes about the positive definitiveness of covariance matrix, most of which have been restricted to the Covariance-matrix of continuous sample.

然而，目前国内外有关协方差矩阵正定性的研究结果并不多，并且大多是集中在连续型样本协方差矩阵方面。

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However, these methods are estimated according to sample variance and covariance estimators of returns.

然而，这些方法都是根据样本的变异数或报酬的共变异数估算的估计值。

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However, these methods are estimated according to sample variance and covariance estimators of returns.

然而，这些方法都是根据样本的变异数或报酬的共变异数估算的估计值。

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