A new multi-sensor optimal information fusion criterion weighted by scalars is presented in the linear minimum variance sense.
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 algorithm 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。
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。
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.
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。
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 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。
应用推荐