Multi-sensor information fusion state estimation problem for descriptor discrete-time stochastic linear systems is studied.
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。
The problem of multi-sensor information fusion state estimation for descriptor discrete-time stochastic linear systems is considered.
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 method of state estimation is discussed, when radars have different observation dimension in one distributed data fusion system with feedback.
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。
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。
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.
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 paper presents the effect of the common process noise on track statistical distance and performance of state estimation fusion.
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。
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。
The exchange of two nonlinear Kalman filters was used to improve the fusion accuracy in the state estimation.
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。
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 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.
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。
On the other hand, state estimation plays an important role in systems and control theory, signal processing and information fusion.
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。
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。
The experimental results show that data fusion is applicable to traffic state estimation.
实例证明，数据融合适用于城市道路交通状态估计。
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.
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。
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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