研究了广义离散随机线性系统的多传感器信息融合状态估计问题。
Multi-sensor information fusion state estimation problem for descriptor discrete-time stochastic linear systems is studied.
考虑了广义离散随机线性系统的多传感器信息融合状态估计问题。
The problem of multi-sensor information fusion state estimation for descriptor discrete-time stochastic linear systems is considered.
论述了带反馈分布式信息融合系统中传感器观测维数不同时的状态估计方法。
The method of state estimation is discussed, when radars have different observation dimension in one distributed data fusion system with feedback.
该算法能充分利用纸机系统的各状态之间的强耦合性，利用纸机系统的可测量信息，融合多个测量输出估计纸机的某一个状态。
The algorithm can fully utilize the coupling performance between different states and the measurable information and fuse several outputs to estimate one state of paper machine.
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.
该文提出一个有效的基于径向基函数神经网络的模型和状态数据融合的汽轮发电机智能估计方法。
An efficient model based on radial basis function neural network and intelligent estimating method for data fusion of the turbine-generator is presented.
本文指出了数据融合中最为关键的几个问题——数据关联、状态估计和目标识别并围绕它们进行了深入的研究。
This dissertation points out the most pivotal problems in data fusion, i. e., data association, state estimation and target recognition, which are investigated in depth.
另一方面，状态估计问题在系统与控制理论、信号处理与信息融合中有很重要的应用。
On the other hand, state estimation plays an important role in systems and control theory, signal processing and information fusion.
其航迹融合过程包括低层次上的状态和属性估计及高层次上的战场态势和威胁评估。
Its track fusion process includes states and attributes estimation of low levels, it also includes battlefields situation and threaten evaluation of high levels.
本文研究动态系统过程噪声对航迹统计距离和状态融合估计性能的影响。
This paper presents the effect of the common process noise on track statistical distance and performance of state estimation fusion.
提出了利用多传感器数据融合的方法估计无人机的状态参数。
The method of utilizing multisensor data fusion to estimate UAV state parameters is proposed.
在初步分离结果的基础上，利用多模型融合滤波算法对故障进行精确定位并得到状态和故障的融合估计。
Then based on the primary isolation result, the fault is accurately located by the multiple-model fusion filtering algorithm, and the fused state and fault estimates are obtained as well.
在作状态估计时，采用两组非线性卡尔曼滤波切换提高融合精度。
The exchange of two nonlinear Kalman filters was used to improve the fusion accuracy in the state estimation.
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.
基于多传感器多模型信息，给出了目标状态基于全局信息融合估计的一种新算法，并通过计算机仿真验证了这种算法的有效性。
Based on Multi_sensor Multi_model information, we present a new algorithm based on total information fusion estimation on target state. We prove the validity of this algorithm by computer.
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.
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.
融合预报协方差矩阵的自适应调整和状态空间鲁棒性估计，给出辨识戴维南等值参数的新算法。
Based on the robust estimation in the state-space and the adaptive modification of prediction covariance matrix, a new algorithm was proposed to track Thevenin equivalent parameters.
实例证明，数据融合适用于城市道路交通状态估计。
The experimental results show that data fusion is applicable to traffic state estimation.
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.
与各局部估计以及状态向量按标量加权融合估计相比，分量融合滤波具有更高的精度。
Compared with all local filters and the fusion filter weighted by scalars for the state vector, the component fusion filter weighted by scalars has higher precision.
另一类在估计目标状态时并没有利用数据融合。
The other estimates target state without explicit use of a data association algorithm.
本文的研究内容为多传感器信息融合理论中的状态融合估计理论，主要针对精确估计的实际应用中，状态融合估计理论存在的一些问题提出了解决方法。
This dissertation considers state fusion estimation of multisensor information fusion theory. The main work of here is to solve the problems when fusion estimation theory is applied in practice.
深入研究了多传感器数据融合的理论方法，并实验研究了数据融合的估计理论在光电经纬仪中的应用，目的是实现对目标状态的预测。
The theory method of multi-sensor data fusion is studied deep. And more, the estimate theory of data fusion is applied concretely to the theodolite with the aim of prognosticating the object state.
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.
用UK -GMPHDF完成局部传感器的局部状态估计，然后用FCM算法对这些局部状态进行融合处理，产生目标的全局状态估计。
In the algorithm, the UK-GMPHDF is used to complete local state estimation of local sensors, then the FCM algorithm is used to fuse the local state estimation and result global state estimation.
用UK -GMPHDF完成局部传感器的局部状态估计，然后用FCM算法对这些局部状态进行融合处理，产生目标的全局状态估计。
In the algorithm, the UK-GMPHDF is used to complete local state estimation of local sensors, then the FCM algorithm is used to fuse the local state estimation and result global state estimation.
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