This article established the covariance matrix method of non-stationary random vibration.
本文提出求解非平稳随机振动问题的一种新方法——协方差矩阵法。
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.
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。
A new method for human face image scale and direction estimation is proposed using covariance matrix eigenvalue and eigenvector of edge set, which is proved by theory and experiments.
提出了利用边缘点集的协方差矩阵的特征值与特征矢量作为人脸图像尺度与方向的粗估计方法，从理论和实验上证明了该方法的可行性。
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.
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。
An expression for the mean and covariance matrix of normal random matrix polynomial is derived by applying the method of matrix differentiation to generating function.
本文应用对母函数微分的方法得到正态随机矩阵多项式的均值与协差阵的表达式。
To deal with the problem, a new method of focusing transformation based on differentiating covariance matrix is proposed to eliminate noise, and hence reduce the focusing error.
为了处理这个问题，提出了一个新的方法，聚焦变换的基础上鉴别协方差矩阵，以消除噪声，并因此减少聚焦误差。
After inspecting the properties of the covariance matrix, a recursive method of system parameter estimation is derived upon LSE.
在研究协方差矩阵特性的基础上，给出一种最小二乘辨识系统的递阶算法。
In this paper, by using the special structure of the covariance matrix, a fast high resolution method based on QR decomposition is presented.
利用阵列输出协方差矩阵的特殊结构，本文提出一种基于QR分解的高分辨谱估计方法及其改进形式。
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…
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。
A new method of removing noise, which is based on multitarget array covariance matrix of received signals, is developed in this paper. The results obtained by the method are simulated by the computer.
本文提出一种新的多目标阵列接收信号协方差矩阵的去噪方法，并对结果进行了计算机模拟。
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。
This method estimates filter gain matrix K directly, avoids calculating system noise covariance matrix Q and measure noise covariance matrix r, it improves stability of the system.
这种方法采用直接估计增益K，避免了求解系统噪声方差阵q和量测噪声方差阵r，使系统的稳定性增强。
But in the very low SNR circumstance, because of the covariance matrix of the observed signals being singularity, the ICA denoising method can not be used.
但研究发现在极低信噪比，由于观测信号的样本协方差矩阵具有奇异性，这使得ICA去噪算法中的白化处理步骤无法进行。
But in the very low SNR circumstance, because of the covariance matrix of the observed signals being singularity, the ICA denoising method can not be used.
但研究发现在极低信噪比，由于观测信号的样本协方差矩阵具有奇异性，这使得ICA去噪算法中的白化处理步骤无法进行。
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