Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method
来源期刊：中南大学学报(英文版)2015年第4期
论文作者：YANG Hai LI Wei LUO Cheng-ming
文章页码：1324 - 1333
Key words：inertial navigation system (INS); wireless sensor network (WSN); mobile target; integrated positioning; fuzzy adaptive; Kalman filter
Abstract: 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.
YANG Hai(杨海), LI Wei(李威), LUO Cheng-ming(罗成名)
(School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China)
Abstract: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.
Key words:inertial navigation system (INS); wireless sensor network (WSN); mobile target; integrated positioning; fuzzy adaptive; Kalman filter
