在卫星的状态估计过程中应用推广的序列估计算法，借助数值积分方法积分状态向量和协方差矩阵。
For the estimation of satellite state, the extended sequential estimation algorithm was applied. The numerical method was used to integrate state vector and error covariance matrix.
本文的目的在于，对于线性平稳时间序列的样本、自协方差、自相关和偏相关函数的渐近性质，给出一个比较系统的描述。
The aim of this paper is to give a systematic account of asymptotic properties of the sample autocovariance, autocorrelation and partial autocorrelation functions of linear stationary time series.
在高斯假定下得到非平稳时间序列的协方差矩阵的转移形式。对一个实际的地震过程进行的数字研究结果证明本文方法是有效的。
The transition of the covariance matrix of the nonstationary time series is obtained with Gaussian assumptions. An actual earthquake is studied by the method proposed and satisfactory res…
这种方法通过由相关函数抽样序列形成的协方差矩阵控制序列的相关性，适用于仿真具有不同概率密度函数的各种有限长相关的随机序列。
This method can control the series correlation via covariance matrix that was formed from correlation series and is suit for simulating finite correlated random series of different distributions.
采用线性插值及双线性插值得到预测点位置上的本征模态值。 结构由原风压场协方差分析得到的主坐标和上述新本征模态值获得未布置测压点位置的风压时间序列。
The linear interpolation and bilinear interpolation were employed to obtain the values of the proper modes on locations where the wind pressure time series are to be predicted.
采用线性插值及双线性插值得到预测点位置上的本征模态值。 结构由原风压场协方差分析得到的主坐标和上述新本征模态值获得未布置测压点位置的风压时间序列。
The linear interpolation and bilinear interpolation were employed to obtain the values of the proper modes on locations where the wind pressure time series are to be predicted.
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