The steady-state optimal estimation of singular systems is studied by applying ARMA innovation model.
基于多项式ARMA新息模型方法提出了随机奇异线性离散时间系统的稳态最优估计。
One of the algorithms is to linearized the state equation based on the optimal estimation.
其中一种方法是基于当前最优估计值对状态方程进行线性化。
The optimal estimation algorithm of measurement noise and the optimal state fusion algorithm for multi-channel system with multiplicative noises are mainly researched in this dissertation.
本文主要针对多通道带乘性噪声系统的观测噪声最优估计算法和状态最优融合估计算法展开进一步研究。
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 results obtained in this paper are very important for the further studies on the state estimation and optimal control of 2-D linear systems with stochastic input.
这些结果对进一步展开对随机2-D系统的状态估计，最优控制和其它方面的研究都具有重要意义。
The formulas of computing the variance and cross-covariance matrices among local state estimation errors are presented, which are applied to compute the optimal weights.
为了计算最优加权，提出了状态估计误差方差阵和互协方差阵的计算公式。
Conclusion Optimal recursive estimation for predicting and filtering of state for this singular system are obtained.
结论得到了该系统状态的最优预测和滤波递推方程。
This paper deals with the optimal state estimation problem for the discrete stochastic system based on the innovation theory and projection method.
基于射影理论及新息分析方法，讨论离散随机线性系统最优状态估计问题。
After performing Kalman filter, the optimal state estimation can be obtained.
进行卡尔曼滤波后，可以获得系统状态最优估计值。
The Predictive Filter is an estimation method based on nonlinear system model, which determines the optimal model error using a one-step ahead control approach to provide accurate state estimations.
预测滤波器是一种基于非线性系统模型的滤波方法，它通过使输出一步前向预测误差最小来估计模型误差，具有较高的估计精度。
This proposed modeling idea can also be applied to dynamic reactive optimization, optimal power flow and state estimation for a faster calculation.
这种建立模型的思想还可以应用到需要计算海森矩阵的动态无功优化、最优潮流以及状态估计等问题的算法中，以提高其计算速度。
Combining the separation principle with a direct construction method, we get the optimal control which is the linear feedback of the state filtering estimation.
结合分离原理和一种直接构造的方法，我们得到了显式的最优控制，它是状态滤波估计的线性反馈。
This paper first introduced the kalman filter, to all sorts of navigation data information fusion, thus constituting navigation system, in order to get the optimal estimation system state.
本文首先介绍了卡尔曼滤波器，对各种导航数据进行信息融合，从而组成导航系统，以获取系统状态的最优估计。
Primary filter accomplishes the fusion of public state vectors about sub filters and time updating, and outputs the credible, precise and optimal estimation of navigation parameter error.
主滤波器(全局滤波器)进行子滤波器的公共状态矢量融合和时间更新，输出可靠、准确的导航参数误差的全局最优估计量。
Primary filter accomplishes the fusion of public state vectors about sub filters and time updating, and outputs the credible, precise and optimal estimation of navigation parameter error.
主滤波器(全局滤波器)进行子滤波器的公共状态矢量融合和时间更新，输出可靠、准确的导航参数误差的全局最优估计量。
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