摘要
针对时变环境带来的传感器数据异常、未知环境干扰等影响,导致基于无迹卡尔曼滤波的动力定位状态估计方法估计精度下降的问题,提出了一种鲁棒无迹卡尔曼滤波方法,该方法通过引入一种基于指数加权的观测噪声协方差矩阵R自适应更新模块和一种基于卡方分布假设检验方法的过程不确定性识别模块处理传感器数据异常情况并估计未知环境力.最后,以某平台供应船的船模为仿真对象,进行了仿真对比实验.仿真结果表明,鲁棒无迹卡尔曼滤波能够准确及时地识别传感器数据异常情况,相较于传统无迹卡尔曼滤波而言,鲁棒无迹卡尔曼滤波状态估计精度更高,收敛速度更快,表现出较强的鲁棒性.
Aiming at the problem of abnormal sensor data and unknown environmental interference caused by time-varying environment,which causes the estimation accuracy of state estimation method for dynamic positioning based on unscented Kalman filter to decrease,a robust unscented Kalman filter method is proposed.The method adopts an exponentially weighted observation noise covariance matrix R adaptive update module and a process uncertainty recognition module based on the chi-squared distribution hypothesis test method to handle sensor data anomalies and estimate unknown environmental forces.Finally,a simulation experiment is carried out with the ship model of a platform supply ship as the simulation object.Simulation results show that the robust unscented Kalman filter can accurately and timely identify anomalies in sensor data.Compared with the traditional unscented Kalman filter,the robust unscented Kalman filter has higher state estimation accuracy,faster convergence and better robustness.
作者
蒋帆
徐海祥
冯辉
卜瑞波
JIANG Fan;XU Haixiang;FENG Hui;BU Ruibo(School of Transportation, Wuhan University of Technology, Wuhan 430063, China;Key Laboratory of High Performance Ship Technology of Ministry of Education, Wuhan University of Technology, Wuhan 430063, China)
出处
《大连理工大学学报》
EI
CAS
CSCD
北大核心
2020年第6期610-618,共9页
Journal of Dalian University of Technology
基金
国家自然科学基金资助项目(51879210,51979210)
中央高校基本科研业务费专项资金资助项目(2019III040,2019III132CG)
武汉理工大学研究生优秀学位论文培育项目(2018-YS-019).
关键词
动力定位
状态估计
鲁棒无迹卡尔曼滤波
传感器数据异常
假设检验
dynamic positioning
state estimation
robust unscented Kalman filter
abnormal sensor data
hypothesis test
作者简介
蒋帆(1994-),男,硕士生,E-mail:987079298@qq.com;徐海祥(1975-),男,博士,教授,博士生导师,E-mail:qukaiyang@163.com.
