在这种情况下，最优加权最小二乘估计变成关于观测和输入的非线性估计，且与线性最小方差估计不可比。
In this case optimally weighted LS estimate is not a linear estimate of a parameter given input and observation anymore and can not be compared with linear minimum variance estimate.
在信号处理、控制和通讯等技术领域，常常使用线性最小方差估计和最优加权最小二乘估计对参数作出估计。
Linear minimum variance estimate and optimally weighted LS estimate are often used in many fields such as signal processing, control and communications.
基于线性无偏最小方差估计理论，提出了一种任意相关噪声异类传感器非线性系统状态矢量融合算法。
Based on linear unbiased minimum variance estimation theory, a fusion algorithm which fused the state vector of nonlinear systems with dissimilar sensors with arbitrary correlated noises is developed.
卡尔曼滤波是一种线性最小方差状态估计，把它有效地结合阵列天线与多用户检测。
Kalman filter is a linear minimum variance state estimator, and it combined array antenna and multiuser detection effectively.
这一类分布族的参数估计可用无偏估计，一致最小方差无偏估计和最优线性无偏估计。
The estimation of this class of the distributed group can be done by the unbiased estimation, uniformly minimum variance estimation and the optimal linear unbiased estimation.
在线性无偏最小方差估计准则下，推导出了该离散化后所得系统的全局最优递推状态估计算法。
In the sense of linear unbiased minimum variance estimation, a global optimal recursive state estimation algorithm for this discretized linear system is proposed.
线性无偏最小方差估计与最优加权最小二乘估计是线性模型下两种最常用的估计方法。
The linear unbiased minimum variance estimate and the optimally weighted least squares estimate are two of the most popular estimation methods for a linear model.
基于线性无偏最小方差估计理论，提出了一种任意相关噪声异类传感器非线性系统状态矢量融合算法。
Based on the linear unbiased minimum variance estimation theory, an asynchronous fusion algorithm that fused the state vector of linear system with arbitrary correlated noises is developed.
在给定的线性模型下，讨论了最优加权最小二乘估计与线性无偏最小方差估计性能比较。
The discussion on the property comparison between optimally weighted LS estimate and linear unbiased minimum variance estimate for a linear model is presented.
在给定的线性模型下，讨论了最优加权最小二乘估计与线性无偏最小方差估计性能比较。
The discussion on the property comparison between optimally weighted LS estimate and linear unbiased minimum variance estimate for a linear model is presented.
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