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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