本文研究了一般的随机效应多元线性模型中线性可估函数的最优线性无偏估计。
In this paper we investigated optimal linear unbiased estimation of the linear estimable function in general multivariate random effect linear model.
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.
针对机动目标跟踪中常见的量测转换问题，提出了一种基于球坐标系下最优线性无偏估计滤波的交互多模型算法。
Aiming at the problem of measurement conversion within the target tracking, a new algorithm combined best linear unbiased estimation with interacting multiple model methods is derived.
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.
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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