A decentralized optimal information fusion Kalman multi -step predictor with a two -layer fusion structure is proposed based on the fusion algorithm weighted by scalars in the linear minimum variance sense for stochastic singular systems measured by multiple sensors.
对带多传感器随机奇异系统,基于线性最小方差标量加权融合算法,给出了具有两层融合结构的多传感器分布式最优信息融合Kalman 多步预报器。
参考来源 - 随机奇异系统多传感器信息融合Kalman多步预报器·2,447,543篇论文数据，部分数据来源于NoteExpress
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.
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