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1)  noise covariance
噪声协方差
1.
Through the analysis of these disturbances their error model is got,the error is compesated by using noise covariance analysis and Kalman filter.
针对卡尔曼滤波器在基于MEMS传感器构成的捷联惯导系统(SINS)误差补偿中的应用,系统分析了微惯性器件组成的载体在运动中所受到的干扰,通过对这些干扰的分析得出其误差模型,采用噪声协方差分析方法,利用卡尔曼滤波对其误差进行补偿。
2)  noise variance
噪声方差
1.
New Method of Image De-noising through Wavelet Shrinkage Based on Estimate of Noise Variance;
基于噪声方差估计的小波阈值图像去噪新方法
2.
The estimator presents both the estimate of instantaneous RDS and the estimates of noise variance, channel power and SNR of current channel with low computational comp.
该估计器能以较低的计算复杂度给出RDS、噪声方差、信道功率和信噪比等多个实时信道参数的准确估计。
3.
An adaptive algorithm of noise variance self-learning and optimization is presented,which utilizes not only the thought of the estimation of the MLE and spatial-temporal synthetical analysis,but also the recursive characteristic of kalman filtering algorithm.
简单介绍了卡尔曼滤波算法,针对噪声方差预先设定对卡尔曼滤波算法及改进算法的不利影响,运用极大似然估计与时空综合分析思想相结合,同时利用卡尔曼滤波算法的递推特性,推导出了一种滤波过程中噪声方差自学习优化的自适应滤波算法。
3)  variance of noise
噪声方差
1.
The fixed point iteration is employed when we restore the image by total variation,and we propose to select the regularization parameter by variance of noise in the iteration.
采用定点迭代进行变分图像恢复并在这个计算框架下提出利用噪声方差选择规整化参数的方法。
4)  measurement noise covariance
观测噪声协方差阵
5)  state yawp covariance matrix
状态噪声协方差矩阵
1.
Through analyzing the kalman filter s capability and character ,Uniting with port supervising radar fact, this dissertation makes MATLAB experiment to state yawp covariance matrix infection to filter resultant, dvances a way of optimizing radar precision , strengthening tracking stabilization ,and .
本文通过对基础卡尔曼滤波的描述,结合港口监管雷达所监管的目标的实际情况,针对于状态噪声协方差矩阵对卡尔曼滤波结果的影响做了仿真,提出了通过准备多组状态噪声协方差矩阵供选择使用以求优化滤波精度,加快收敛速度,加强跟踪稳定度的方法。
6)  Heteroscedastic noise
异方差噪声
补充资料:并合方差
分子式:
CAS号:

性质:是按加权方式计算出的各组的共同方差,当试验分m组进行,总的测定方差s2由m组的方差si2(i=1,2, …,m)共同决定。总方差,式中ni、fi和xi;分别为第i组的测定次数、自由度和平均值,xij是第i组的第j次测定值。

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