An enhanced extended Kalman filtering （E2KF） algorithm is proposed in this paper to cope with the joint multiple carrier frequency offsets （CFOs） and time-variant channel estimate for MIMO-OFDM systems over high m...An enhanced extended Kalman filtering （E2KF） algorithm is proposed in this paper to cope with the joint multiple carrier frequency offsets （CFOs） and time-variant channel estimate for MIMO-OFDM systems over high mobility scenarios. It is unveiled that, the auto-regressive （AR） model not only provides an effective method to capture the dynamics of the channel parameters, which enables the prediction capability in the EKF algorithm, but also suggests an method to incorporate multiple successive pilot symbols for the improved measurement update.展开更多
The performance of the conventional Kalman filter depends on process and measurement noise statistics given by the system model and measurements.The conventional Kalman filter is usually used for a linear system,but i...The performance of the conventional Kalman filter depends on process and measurement noise statistics given by the system model and measurements.The conventional Kalman filter is usually used for a linear system,but it should not be used for estimating the state of a nonlinear system such as a satellite motion because it is difficult to obtain the desired estimation results.The linearized Kalman filtering approach and the extended Kalman filtering approach have been proposed for a general nonlinear system.The equations of satellite motion are described.The satellite motion states are estimated,and the relevant estimation errors are calculated through the estimation algorithms of the both above mentioned approaches implemented in Matlab are estimated.The performances of the extended Kalman filter and the linearized Kalman filter are compared.The simulation results show that the extended Kalman filter is much better than the linearized Kalman filter at the aspect of estimation effect.展开更多
We introduce the extended Kalman filter(EKF)method combined with the least square estimation to identify the unknown load acting on the time-varying structure and realize the tracking of the structural parameters of t...We introduce the extended Kalman filter(EKF)method combined with the least square estimation to identify the unknown load acting on the time-varying structure and realize the tracking of the structural parameters of the time-varying system.Firstly,we propose the dynamic load identification method when the unknown parameters are stiffness coefficients.Then,a five-degree-of-freedom slowly-varying-stiffness structure is introduced to verify the effectiveness and the accuracy of the EKF method.The results show that the EKF method can accurately identify unknown loads and structural parameters simultaneously even considering noises in the input data.展开更多
基金Supported by the National Natural Science Foundation of China(61873323,61773174,61573162)the Wuhan Science and Technology Plan Project(2018010401011292)+2 种基金the Hubei Province Natural and Science Foundation(2017CFB4165,2016CFA037)the Open Fund Project of Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System(znxx2018ZD02)the Basic Research Project of Shenzhen(JCYJ20170307160923202,JCYJ20170818163921328)
文摘An enhanced extended Kalman filtering （E2KF） algorithm is proposed in this paper to cope with the joint multiple carrier frequency offsets （CFOs） and time-variant channel estimate for MIMO-OFDM systems over high mobility scenarios. It is unveiled that, the auto-regressive （AR） model not only provides an effective method to capture the dynamics of the channel parameters, which enables the prediction capability in the EKF algorithm, but also suggests an method to incorporate multiple successive pilot symbols for the improved measurement update.
文摘The performance of the conventional Kalman filter depends on process and measurement noise statistics given by the system model and measurements.The conventional Kalman filter is usually used for a linear system,but it should not be used for estimating the state of a nonlinear system such as a satellite motion because it is difficult to obtain the desired estimation results.The linearized Kalman filtering approach and the extended Kalman filtering approach have been proposed for a general nonlinear system.The equations of satellite motion are described.The satellite motion states are estimated,and the relevant estimation errors are calculated through the estimation algorithms of the both above mentioned approaches implemented in Matlab are estimated.The performances of the extended Kalman filter and the linearized Kalman filter are compared.The simulation results show that the extended Kalman filter is much better than the linearized Kalman filter at the aspect of estimation effect.
基金supported in part by the National Natural Science Foundation of China(No.51775270)the Project of Qatar National Research Fund(No.NPRP11S-1220-170112)
文摘We introduce the extended Kalman filter(EKF)method combined with the least square estimation to identify the unknown load acting on the time-varying structure and realize the tracking of the structural parameters of the time-varying system.Firstly,we propose the dynamic load identification method when the unknown parameters are stiffness coefficients.Then,a five-degree-of-freedom slowly-varying-stiffness structure is introduced to verify the effectiveness and the accuracy of the EKF method.The results show that the EKF method can accurately identify unknown loads and structural parameters simultaneously even considering noises in the input data.
