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.
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.
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.
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.
卡尔曼滤波，是线性、无偏、最小方差的实时递推滤波，是一种高效、优化的数据处理方法。
Kalman filter is a high efficiency, optimal data process method, which is a realtime recursive filter of linear, non-bias and least square.
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.
卡尔曼滤波，是线性、无偏、最小方差的实时递推滤波，是一种高效、优化的数据处理方法。
Kalman filter is a high efficient, optimal data process method, which is a real time recursive filter based on linear, non-bias and least square approach.
卡尔曼滤波，是线性、无偏、最小方差的实时递推滤波，是一种高效、优化的数据处理方法。
Kalman filter is a high efficient, optimal data process method, which is a real time recursive filter based on linear, non-bias and least square approach.
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