Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method  

作　　者：杨海 李威 罗成名 

机构地区：[1]School of Power and Mechanical Engineering, Wuhan University

出　　处：《Journal of Central South University》2015年第4期1324-1333,共10页中南大学学报（英文版）

基　　金：Project(2013AA06A411)supported by the National High Technology Research and Development Program of China;Project(CXZZ14_1374)supported by the Graduate Education Innovation Program of Jiangsu Province,China;Project supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China

摘　　要：Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.Pure inertial navigation system（INS） has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network（WSN） associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter（KF） as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system（FIS）, and the fuzzy adaptive Kalman filter（FAKF） based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.

关 键 词：inertial navigation system（INS） wireless sensor network（WSN） mobile target integrated positioning fuzzy adaptive Kalman filter 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置] TN929.5[自动化与计算机技术—控制科学与工程]