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。
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。
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。
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。
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。
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。
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