程鉴皓1, 2,,,
杨菊花3,
刘昊1, 2,
张琳婧1, 2
1.兰州交通大学自动控制研究所 ??兰州 ??730070
2.甘肃省高原交通信息工程及控制重点实验室 兰州 730070
3.兰州交通大学交通运输学院 ??兰州 ??730070
基金项目:国家自然科学基金(61863024),甘肃省基础研究创新群体计划(1606RJIA327),甘肃省高等学校科研项目(2018C-11),甘肃省自然科学基金(18JR3RA107),甘肃省科技计划资助(18CX3ZA004)
详细信息
作者简介:陈光武:男,1976年生,博士后,研究方向为惯性导航和组合导航
程鉴皓:男,1995年生,硕士生,研究方向为惯性导航和组合导航
杨菊花:女,1978年生,博士,研究方向为交通运输工程
刘昊:男,1995年生,硕士生,研究方向为惯性导航和组合导航
张琳婧:女,1994年生,硕士生,研究方向为惯性导航和组合导航
通讯作者:程鉴皓 cjhwww2005@163.com
中图分类号:TN967.2计量
文章访问数:1725
HTML全文浏览量:621
PDF下载量:50
被引次数:0
出版历程
收稿日期:2018-12-19
修回日期:2019-04-22
网络出版日期:2019-05-22
刊出日期:2019-07-01
Improved Neural Network Enhanced Navigation System of Adaptive Unsented Kalman Filter
Guangwu CHEN1, 2,Jianhao CHENG1, 2,,,
Juhua YANG3,
Hao LIU1, 2,
Linjing ZHANG1, 2
1. Automatic Control Institute, Lanzhou Jiaotong University, Lanzhou 730070, China
2. Gansu Provincial Key Laboratory of Traffic Information Engineering and Control, Lanzhou 730070, China
3. School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Funds:The National Natural Science Foundation of China (61863024), The Gansu Province Basic Research Innovation Group Program (1606RJIA327), The Gansu Province Higher Education Research Project (2018C-11), The Gansu Province Natural Science Foundation (18JR3RA107), The Gansu Province Science and Technology Plan Funding (18CX3ZA004)
摘要
摘要:基于微机电系统(MEMS)的惯性器件和全球定位系统(GPS)的组合导航系统在卫星信号失锁时存在误差发散的问题,该文提出一种基于人工蜂群算法(ABC)改进的径向基函数(RBF)神经网络增强改进的自适应无迹卡尔曼滤波算法(AUKF)。在GPS信号失锁的情况下利用训练好的神经网络输出预测信息来对捷联惯导系统进行误差校正。最后通过车载半实物仿真实验验证该方法的性能。实验结果表明该方法在失锁情况下对于捷联惯导系统的误差发散有较为明显的抑制效果。
关键词:组合导航/
径向基神经网络/
无迹卡尔曼滤波/
GPS故障
Abstract:In order to solve the problem of speed and position error divergence in the integrated navigation system based on MicroElectro Mechanical Systems (MEMS) inertial device and GPS system combined positioning, an improved Adaptive Unsecnted Kalman Filter (AUKF) enhanced by the Radial Basis Function(RBF) neural network based on Artificial Bee Colony(ABC) algorithm is proposed. When the GPS signal is out of lock, the trained network outputs predictied information to perform error correction on the Strapdown Inertial Navigation System(SINS). Finally, the performance of the method is verified by vehicle-mounted semi-physical simulation experiments. The experimental results show that the proposed method has a significant inhibitory effect on the error divergence of the strapdown inertial navigation system in the case of loss of lock.
Key words:Intergrated navigation/
Radial basis neural network/
Unscented Kalman Filter(UKF)/
GPS break down
PDF全文下载地址:
https://jeit.ac.cn/article/exportPdf?id=9ff96d60-2674-4f99-9e22-c4259b7f222c