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
Deployment of nodes based on K-barrier coverage in an underground wireless sensor network is described. The network has automatic routing recovery by using a basic information table (BIT) for each node. An RSSI positi...Deployment of nodes based on K-barrier coverage in an underground wireless sensor network is described. The network has automatic routing recovery by using a basic information table (BIT) for each node. An RSSI positioning algorithm based on a path loss model in the coal mine is used to calculate the path loss in real time within the actual lane way environment. Simulation results show that the packet loss can be controlled to less than 15% by the routing recovery algorithm under special recovery circum- stances. The location precision is within 5 m, which greatly enhances performance compared to tradi- tional frequency location systems. This approach can meet the needs for accurate location underground.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.
基金supported by the National Key Technology R&D Program of China (No. 2008BAH37B05095)
文摘Deployment of nodes based on K-barrier coverage in an underground wireless sensor network is described. The network has automatic routing recovery by using a basic information table (BIT) for each node. An RSSI positioning algorithm based on a path loss model in the coal mine is used to calculate the path loss in real time within the actual lane way environment. Simulation results show that the packet loss can be controlled to less than 15% by the routing recovery algorithm under special recovery circum- stances. The location precision is within 5 m, which greatly enhances performance compared to tradi- tional frequency location systems. This approach can meet the needs for accurate location underground.
