【作者】 于飞； 刘佳兴； 奔粤阳；

【Author】 YU Fei,LIU Jia-xing,BEN Yue-yang (College of Automation,Harbin Engineering University,Harbin 150001)

【机构】 哈尔滨工程大学自动化学院；

【摘要】 目前,卡尔曼滤波器(KF)被用于融合惯性导航系统(INS)和全球定位系统(GPS)的数据以提供位置,速度和姿态信息。然而,卡尔曼滤波器(KF)存在一些缺点,像抗噪声能力弱,对预定义的误差模型的依赖性强等。文章提出并讨论了应用自适应神经网络模糊推理系统(ANFIS)对应用INS和GPS的导航系统数据融合的实时实现,并在几个人为引进的GPS的中断(500秒)的时间段内评估该系统。仿真结果表明,基于ANFIS的INS/GPS数据融合技术能够保证系统的实时定位精度。更多还原

【Abstract】 Presently,Kalman Filter(KF) is used to fuse data from both inertial navigation systems(INS) and global positioning systems(GPS) to provide position,velocity and attitude information.However, several drawbacks associated with KF like its immunity to noise,its dependency on predefined errors models.This study proposes and discusses the implementation of adaptive neuro-fuzzy inference system (ANFIS) to fuse INS and GPS data for navigation applications.The system is evaluated while considering several intentionally introduced GPS outages for periods of 500 seconds.The simulated results show that ANFlS-based model for INS/GPS data fusion could ensure system position accuracy in real-time.更多还原