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 mobil...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.展开更多
To achieve the satellite formation control and the succeed formation missions, we present a new stealthy method to determine the relative states between formation satellites. In this method, the combination of a CCD c...To achieve the satellite formation control and the succeed formation missions, we present a new stealthy method to determine the relative states between formation satellites. In this method, the combination of a CCD camera and laser radar is used as the relative measure sensors. To reduce electromagnetic radiation, the laser radar works intermittently to minimize the probability of being discovered. And an unscented Kalman filter (UKF) is applied to estimate the relative states. The observability of this method is analyzed. The validity and effectiveness of the method is demonstrated in a typical application of formation relative navigation.展开更多
基金Project(2013AA06A411)supported by the National High Technology Research and Development Program of ChinaProject(CXZZ14_1374)supported by the Graduate Education Innovation Program of Jiangsu Province,ChinaProject supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘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.
文摘To achieve the satellite formation control and the succeed formation missions, we present a new stealthy method to determine the relative states between formation satellites. In this method, the combination of a CCD camera and laser radar is used as the relative measure sensors. To reduce electromagnetic radiation, the laser radar works intermittently to minimize the probability of being discovered. And an unscented Kalman filter (UKF) is applied to estimate the relative states. The observability of this method is analyzed. The validity and effectiveness of the method is demonstrated in a typical application of formation relative navigation.
