车载GPS/DR组合导航系统卡尔曼滤波方法的改进  

The Improvement on Kalman Filter Method for Vehicle GPS/DR Integrated Navigation System

作　　者：高金辉[1] 汪晓晨[1] 贾利锋[1] 

机构地区：[1]河南师范大学物理与信息工程学院,河南新乡453007

出　　处：《河南师范大学学报（自然科学版）》2009年第2期67-69,共3页Journal of Henan Normal University(Natural Science Edition)

基　　金：河南省教育厅基金项目(2009A510006)

摘　　要：将全球定位系统(GPS)和航位推算法(DR)两种定位方式结合,实现车辆GPS/DR组合定位系统的自适应信息融合.联合卡尔曼滤波器存在数学模型不确定性和误差模型的随机性的缺点,提出改进方法是采用联邦滤波器,并引入利用模糊推理建立的模糊自适应联邦滤波器,提高了系统的精度和功能.Integrating the two position methods： global positioning system and dead reckoning, the data fusion method is set up for Vehicle GPS/DR Integrated Navigation System. Federated Kalman filter has several flaws in mathematical models uncertainty and error models randomicity, The improvement method uses an adaptive federated Kalman filter, drawing in a Fuzzy logic to set up a fuzzy adaptive federated kalman filter, sum up EKF, UKF,PF the three nonlinearity filter on principle of work, the advantages and disadvantages and applicable scope.

关 键 词：信息融合 GPS/DR组合导航 自适应联邦滤波器 

分 类 号：U666.1[交通运输工程—船舶及航道工程]