本文提出适合于两级混合式多传感器系统的全局最优状态估计解。
This paper presents a globally optimal composite filtering solution for a two-level hybrid multisensor system.
然后，对这N个状态估计值进行概率加权求和，得到最优状态估计值。
Then the N state estimates, each of which is weighted by its possibility that is also calculated on-line, are combined to form a optimal estimate.
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。
This paper deals with the optimal state estimation problem for the discrete stochastic system based on the innovation theory and projection method.
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。
One of the algorithms is to linearized the state equation based on the optimal estimation.
基于一定的解码状态，声码器通过最小均方误差（MMSE）估计的方法估计最优参数，充分降低信道误码对重建语音质量的影响。
The minimum mean square error (MMSE) is computed for each decoding state to estimate optimal parameters and to reduce the influence of the bit error.
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 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.
研究了2维隐马尔可夫模型的三个基本问题，包括概率评估问题、最优状态问题和参数估计问题。
The three basic problems of two-dimensional (2-d) hidden Markov models (HMMs) are studied, including probability evaluation, optimal states and parameter estimation.
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。
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.
对系统模型进行了状态最优估计周围的线性化和采样周期的离散化，给出了干扰方程。
Interferential equations are given after system models are transformed around optimization estimate of state to linearization and dispersed according to sampling period.
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。
Combining the separation principle with a direct construction method, we get the optimal control which is the linear feedback of the state filtering estimation.
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。
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.
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.
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.
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。
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.
给出了网络控制系统在不完全状态信息时系统状态的线性最优估计器；
The optimal estimator of system state for networked control systems without full state information is presented.
稳定状态时网络的输出给出了空间信号源方向的最优估计。
The optimal direction estimation of spatial signal sources can be obtained from the output of the network when the network settled.
进行卡尔曼滤波后，可以获得系统状态最优估计值。
After performing Kalman filter, the optimal state estimation can be obtained.
目标通过对自身和导弹的状态估计，估计出相对导弹作不同机动时所获得的效能，并以最大效能为最优机动形式。
By estimating the state of a missile, the efficiency of object maneuvering is gained, and the highest efficiency is taken as the optimized maneuvering.
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。
This proposed modeling idea can also be applied to dynamic reactive optimization, optimal power flow and state estimation for a faster calculation.
结果表明基于改进粒子群算法的状态估计可以得到最优值，但时间较长，检测法可以检测出不良数据。
The results show that the state estimation method based on improved particle swarm algorithm can be the best value, but a longer time, and the bad data can be detected by J(x) detection method.
文中比较了三种融合估计的精度和计算负担，可应用于信息融合状态或信号最优估计。
Their precision and computational burdens are compared. They can be applied into optimal information fusion estimation for the states or signals.
文中比较了三种融合估计的精度和计算负担，可应用于信息融合状态或信号最优估计。
Their precision and computational burdens are compared. They can be applied into optimal information fusion estimation for the states or signals.
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