文章摘要
引用本文:樊龙江,陈安升,陈帅,韩林.自适应UKF在北斗定位算法中的应用[J].导航与控制,2019,(4):95-101 本文二维码信息
二维码(扫一下试试看!)
自适应UKF在北斗定位算法中的应用
Application of Adaptive UKF in Beidou Location Algorithm
  
DOI:
中文关键词:  北斗解算  无迹Kalman滤波  自适应  渐消因子
English Keywords:Beidou position  unscented Kalman filter(UKF)  adaptive  fading factor
基金项目:中国博士后科学基金(编号:2015M580434, 2016T90461);江苏省博士后科研资助计划(编号:1501050B);国防基础科研计划(编号:JCKY2016606B004)
作者单位
樊龙江 上海航天电子技术研究所 上海 201109 
陈安升 北京自动化控制设备研究所 北京 100074 
陈帅 南京理工大学自动化学院南京 210094 
韩林 南京理工大学自动化学院南京 210094 
摘要点击次数: 4
全文下载次数: 9
中文摘要:
      北斗导航系统发展日益成熟,介绍了北斗定位解算与GPS解算的差异,针对扩展Kalman滤波(Extended Kalman Filter,EKF)算法在北斗解算过程中容易引入非线性误差,无迹Kalman滤波(Unscented Kalman Filter,UKF)算法受初值和系统噪声影响较大问题,提出了一种自适应无迹Kalman滤波(Adaptive Unscented Kalman Filter,AUKF)北斗定位解算算法。该算法利用观测残差信息构建自适应渐消矩阵,消除量测噪声异常带来的影响,同时提高了滤波精度。实验表明,与EKF和UKF定位解算算法相比,AUKF算法在定位精度和对系统噪声鲁棒性方面都有所提高,是一种可靠稳定的北斗定位算法。
English Summary:
      The development of BDS is increasingly mature. In this paper, the differences between Beidou positioning solution and GPS positioning solution is described. Due to the fact that extended Kalman filter (EKF) algorithm in solving of Beidou position easily introduces the nonlinear error, the unscented Kalman filter (UKF) algorithm is affected by the systematic noise and the initial value, an adaptive unscented Kalman filter (AUKF) algorithm for Beidou positioning is proposed. This algorithm uses the observation residual information to construct the adaptive fading matrix which can eliminate the influence of the abnormal measurement noise and improve the filtering accuracy. It can be shown that AUKF algorithm has improved the positioning accuracy and the ability to resist the system noise compared with EKF and UKF location algorithm by the experiment. Therefore, AUKF algorithm is a reliable and stable Beidou location algorithm.
查看全文  查看/发表评论  下载PDF阅读器