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样本协方差矩阵

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Traditional multivariable analysis method, for example, principal component analysis method and factor analysis method, are common in calculating the mean vector, the covariance matrix of sample and other variables.

摘要传统的多元统计分析方法,如主成分分析方法和因子分析方法等的共同点是计算样本的均值向量和协方差矩阵,并在这两者的基础上计算其他统计量。

The premise of the superiority of the conventional statistical STAP is to have ample training samples with interference that are independently and identically distributed with interference in the test sample, in order to estimate the covariance matrix.

常规的统计STAP方法性能最优的前提条件是具有足够的与待检测样本中的干扰独立同分布的训练样本,以便估计协方差矩阵。

The commons among those methods is that all of them calculate the mean vector and the covariance matrix of sample then calculate other variables by using them.

这些方法有个共同的地方就是都会计算样本的均值向量和协方差矩阵,并在这两者的基础上计算其他统计量。

In this paper,an improvement is made through selecting a group of normal orthogonal vectors in feature subspace,to generate large amount of virtual training samples.

在模式特征子空间中选取一组标准正交向量,使用这组向量可以生成大量的虚拟训练样本,从而实现对协方差矩阵的优化。

In this paper, an improvement is made through selecting a group of normal orthogonal vectors in feature subspace, to generate large amount of virtual training samples.

摘要在模式特征子空间中选取一组标准正交向量,使用这组向量可以生成大量的虚拟训练样本,从而实现对协方差矩阵的优化。

The method will become invalidation and the scatter matrix will become a zero matrix when any class or per-son has only one training pattern available.

当仅有一个训练样本时,该方法中的协方差矩阵就变成了零矩阵,方法就会失效。

By doing so, the effect of training data pollution in STAP is mitigated.

另一种方法是根据已知的雷达系统参数计算出近程杂波的距离多普勒二维分布,在STAP处理时将包含近程杂波的距离单元剔除使之不参与协方差矩阵的统计,这样就减少了样本污染对STAP的影响。

But when there are outliers in the sample, these methods are easily affected by them.

但是当样本数据中包括离群值时,计算结果就会很容易受到这些离群值的影响,这是因为传统的均值向量和协方差矩阵都不是稳健的统计量。

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