提出一种新的标量加权多传感器线性最小方差意义下的最优信息融合准则。
A new multi-sensor optimal information fusion criterion weighted by scalars is presented in the linear minimum variance sense.
提出了一种新的标量加权线性最小方差意义下的多传感器最优信息融合算法。
A new multi-sensor optimal information fusion algorithm weighted by scalars is presented in the linear minimum variance sense.
通过给出适当的适应度函数，寻找出全局的最优解，并得到配准结果，这为医学临床诊断多模态信息融合提供了一种方法。
By giving suitable fitness function, global optimal solution was found and result of registration was given. A method was provided for clinical diagnosis.
针对组合导航系统在数据处理时存在的计算量大和故障数据相互污染的问题，提出了一种基于信息融合的导航参数最优估计滤波方法。
A new method of optimum navigation parameter estimate based on information fusion is presented in this paper in view of the heavy calculation and faulted data spread in integrated navigation system.
目前在信息融合领域广泛使用的融合算法是卡尔曼滤波，它在线性高斯模型下能得到最优估计，但在非线性非高斯模型下则无法应用。
The Kalman Filter is widely applied in the Information Fusion at the present, which can get the optimal estimate in the Linear-Gaussian model, but not applied in the nonlinear and non-Gaussian model.
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.
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.
应用推荐