简介： 基于模糊逻辑和无偏最小方差估计（UMVE）算法提出一种新的分布式CFAR系统（分布式模糊UMVE），系统中每个UMVE-CFAR检测器根据参考单元计算出映射到虚警空间的隶属函数值，...
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线性无偏最小方差估计 LUMV
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.
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