Thereinto, for the spectral decomposition estimate of the covariance matrix , we can gain the risk functions under some losses.

其中，对于观测向量协方差阵的谱分解估计，我们很容易得到它在一些损失下的风险函数。

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Compared with the traditional method, it does not need disassembling the power spectral densities of stochastic signal covariance and is not bound to if the power spectral densities are rational.

与传统的方法相比，无需对随机信号的协方差函数的功率谱进行分解，不受限于协方差函数的功率谱是否为有理式。

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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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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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