This paper is to study the linear minimum variance estimation for discrete-time systems with instantaneous and two time delayed measurements.
本文研究具有即时观测和两个延迟观测系统的线性最小均方差估计。
In the sense of linear unbiased minimum variance estimation, a global optimal recursive state estimation algorithm for this discretized linear system is proposed.
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。
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.
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。
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。
False Alarm Rate (CFAR) detector (FUCAP) based on Fuzzy logic, Unbiased Minimum-Variance Estimation (UMVE) and Cell Averaging (CA) is presented in this paper.
基于模糊逻辑，无偏最小方差估计(UMVE)和单元平均(CA)提出一种新的恒虚警检测器(FUCAP)。
A new CFAR detector (UMVEM-CFAR) based on Unbiased Minimum-variance Estimation (UMVE) is presented in this paper.
基于无偏最小方差估计(UMVE)算法，提出了一种新的恒虚警检测器(UMVEM - CFAR)。
A new CFAR detector (UMVEM-CFAR) based on Unbiased Minimum-variance Estimation (UMVE) is presented in this paper.
基于无偏最小方差估计(UMVE)算法，提出了一种新的恒虚警检测器(UMVEM - CFAR)。
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