介绍了一种重置协方差阵的递推辨识算法。
A recurrence identification algorithm with co variance matrix reset is presented.
同时给出了任两个传感器之间的预报误差协方差阵的计算公式。
The computation formulas for the prediction error covariance matrix between any two subsystems are given.
为了计算最优加权，提出了局部滤波误差协方差阵的计算公式。
In order to compute the optimal weights, the formula of computing the covariance matrices among local filtering errors is presented.
本文还介绍了隐周期和协方差阵理论以及用它们来分析测井信号。
In this paper, we offer the theories of hide periods and covariance matrix and use them to identify the fluid property of reservoir.
然后，推导了任两个局部估计误差之间的互协方差阵的计算公式。
Then we derive the computation formula for the cross-covariance matrix between any two local estimators.
为了计算最优加权阵，提出了局部估计误差互协方差阵的计算公式。
In order to compute the optimal weighting matrices, the formula of computing the cross-covariance matrix between local estimation errors is presented.
为了计算最优加权阵，提出了计算局部滤波误差互协方差阵的公式。
In order to compute the optimal weighting matrices, the formula of computing the cross-covariance matrices among local filtering errors, is presented.
概率方面，考虑到二阶协方差阵，计及了各节点注入功率的一阶相关性。
In the probabilistic aspect, the second order moment, i. e. covariance was used, which took the first order correlation between nodal powers into account.
为了计算最优加权，提出了局部估计误差方差阵和互协方差阵的计算公式。
In order to compute the optimal weights, the formulas of computing the local estimation error covariance and cross-covariance matrices are presented.
举例说明了具有奇异协方差阵观测值的三角网平差和相关分组平差的实施。
The practical applications of these methods are illustrated by taking triangulation adjustment and that of correlated observations in groups as examples.
为了计算最优加权，提出了状态估计误差方差阵和互协方差阵的计算公式。
The formulas of computing the variance and cross-covariance matrices among local state estimation errors are presented, which are applied to compute the optimal weights.
因为这时模型协方差阵结构仍含有方差参数，因此我们的目标是寻求可行估计。
The variance-covariance matrix still include parameter of variance in this condition, so our purpose is to look for feasible estimations.
这种滤波器的关键是在均方意义下推导无偏转换测量误差协方差阵的最佳估计。
The key of the presented filter is to derive the best estimate of the covariance matrix of the unbiased converted measurements in the mean-square sense.
利用该矩阵滤波器处理实测数据协方差阵，并利用特征分解获得其噪声子空间。
The noise suppression method using matrix filter bssed on matched field processing is presented.
该方法包括：1）将选权拟合的方法应用于确定未知参数及其方差-协方差阵的初值；
The improvement includes: 1) the adaptive filtering by selection of the parameter weights is applied in the parameter estimation;
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。
Thereinto, for the spectral decomposition estimate of the covariance matrix , we can gain the risk functions under some losses.
最后，我们还研究了一般生长曲线模型在不同可预测变量下的简单投影预测关于协方差阵的稳健性。
Finally, robustness of the simple projection predictor in the general growth curve model with different linear predictable variable on the covariance matrix are investigated.
最后给出了平差待估参数与观测值真误差之间的微分关系，由此即可导出平差待估参数的协方差阵。
At last gives the differential relationships between the unknown parameters and the true errors of the observations. These relationships can be used to evaluate the precision of the estimations.
将线性混合模型中随机效应的协方差阵推广为正定阵，运用方差分析估计的方法给出了方差分量的估计。
In this paper, the covariance matrix of random effect in linear mixed model is extended to positive matrix. We construct the estimation of variance components based on the idea of ANOVA estimation.
结果双变量多水平模型可以估计各水平两个变量的方差协方差阵，据此可以计算出相关系数随协变量变化的函数式。
Results Multilevel models can present the variance covariance metrics of two dependent variables in every levels, and make out the functional expresses of correlation coefficient with covariates.
在系统具有正则无穷远极点和奇异无穷远极点的条件下，得到了系统状态向量的均值、方差阵及协方差阵的计算公式。
The state vectors expectation, variance and covariance matrices are given under the conditions of the systems with regular infinite extreme point and singular infinite extreme point.
分析当多元随机变量协方差阵正定时，各随机分量应满足的关系，并结合多项分布研究离散型与连续型样本协方差阵的不同。
And studying the difference of positive defined matrix of discrete and continuous sample by using of mal-distribution and the relationship among weights.
用天线阵归一化广义阻抗矩阵表示单元互耦，分析了考虑单元互耦效应的自适应天线协方差矩阵的特点。
By using the arrays normalized general impedance matrix to express the mutual coupling between the elements, the characteristics of the covariance matrix of the adaptive arrays are analyzed.
方差分量模型的随机效应的协方差为单位阵时《线性模型引论》已进行研究。
Variance component model of the random effects of covariance matrix unit for the "Linear model introduction" matrix has been studied.
基于阵列协方差矩阵特征分解的测向技术，在天线阵各通道特性不一致时，其性能会急剧下降。
For the eigen-based direction finding technique, array sensor gain and phase uncertainties degrade severely its performance.
基于阵列协方差矩阵特征分解的测向技术，在天线阵各通道特性不一致时，其性能会急剧下降。
For the eigen-based direction finding technique, array sensor gain and phase uncertainties degrade severely its performance.
应用推荐