The alignment accuracy of the strap-down inertial navigation system （SINS） of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences base...The alignment accuracy of the strap-down inertial navigation system （SINS） of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.展开更多
In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, ...In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.展开更多
In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate r...In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.展开更多
This paper presents an effective and feasible method for detecting dynamic load-altering attacks(D-LAAs)in a smart grid.First,a smart grid discrete system model is established in view of D-LAAs.Second,an adaptive fadi...This paper presents an effective and feasible method for detecting dynamic load-altering attacks(D-LAAs)in a smart grid.First,a smart grid discrete system model is established in view of D-LAAs.Second,an adaptive fading Kalman filter(AFKF)is designed for estimating the state of the smart grid.The AFKF can completely filter out the Gaussian noise of the power system,and obtain a more accurate state change curve(including consideration of the attack).A Euclidean distance ratio detection algorithm based on the AFKF is proposed for detecting D-LAAs.Amplifying imperceptible D-LAAs through the new Euclidean distance ratio improves the D-LAA detection sensitivity,especially for very weak D-LAA attacks.Finally,the feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified based on simulations.展开更多
Aiming at the problem of relative navigation for non-cooperative rendezvous of spacecraft,this paper proposes a new angles-only navigation architecture using non-linear dynamics method. This method does not solve the ...Aiming at the problem of relative navigation for non-cooperative rendezvous of spacecraft,this paper proposes a new angles-only navigation architecture using non-linear dynamics method. This method does not solve the problem of poor observability of angles-only navigation through orbital or attitude maneuvering,but improves the observability of angles-only navigation through capturing the non-linearity of the system in the evolution of relative motion. First,three relative dynamics models and their corresponding line-of-sight(LoS)measurement equations are introduced,including the rectilinear state relative dynamics model,the curvilinear state relative dynamics model,and the relative orbital elements(ROE)state relative dynamics model. Then,an observability analysis theory based on the Gramian matrix is introduced to determine which relative dynamics model could maximize the observability of angles-only navigation. Next,an adaptive extended Kalman filtering scheme is proposed to solve the problem that the angles-only navigation filter using the non-linear dynamics method is sensitive to measurement noises. Finally,the performances of the proposed angles-only navigation architecture are tested by means of numerical simulations,which demonstrates that the angles-only navigation filtering scheme without orbital or attitude maneuvering is completely feasible through improving the modeling of the relative dynamics and LoS measurement equations.展开更多
Ultra-Wide Bandwidth(UWB)localization based on time of arrival(TOA)and angle of arrival(AOA)has attracted increasing interest owing to its high accuracy and low cost.However,existing localization methods often fail to...Ultra-Wide Bandwidth(UWB)localization based on time of arrival(TOA)and angle of arrival(AOA)has attracted increasing interest owing to its high accuracy and low cost.However,existing localization methods often fail to achieve satisfactory accuracy in realistic environments due to multipath effects and non-line-of-sight(NLOS)propagation.In this paper,we propose a passive anchor assisted localization(PAAL)scheme,where the active anchor obtains TOA/AOA measurements to the agent while the passive anchors capture the signals from the active anchor and agent.The proposed method fully exploits the time-difference-of-arrival(TDOA)information from the measurements at the passive anchors to complement single-anchor joint TOA/AOA localization.The performance limits of the PAAL system are derived as a benchmark via the information inequality.Moreover,we implement the PAAL system on a low-cost UWB platform,which can achieve 20 cm localization accuracy in NLOS environments.展开更多
文摘The alignment accuracy of the strap-down inertial navigation system （SINS） of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61004048 and 61201010)
文摘In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.
基金Supported by the National Natural Science Foundation of China （50979017, NSFC60775060） the National High Technology Ship Research Project of China （GJCB09001）
文摘In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.
基金the Science and Technology Project of the State Grid Shandong Electric Power Company:Research on the vulnerability and prevention of the electrical cyber-physical monitoring system based on interdependent networksthe National Natural Science Foundation of China(61873057)and the Education Department of Jilin Province(JJKH20200118KJ).
文摘This paper presents an effective and feasible method for detecting dynamic load-altering attacks(D-LAAs)in a smart grid.First,a smart grid discrete system model is established in view of D-LAAs.Second,an adaptive fading Kalman filter(AFKF)is designed for estimating the state of the smart grid.The AFKF can completely filter out the Gaussian noise of the power system,and obtain a more accurate state change curve(including consideration of the attack).A Euclidean distance ratio detection algorithm based on the AFKF is proposed for detecting D-LAAs.Amplifying imperceptible D-LAAs through the new Euclidean distance ratio improves the D-LAA detection sensitivity,especially for very weak D-LAA attacks.Finally,the feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified based on simulations.
基金supported by the China Aerospace Science and Technology Corporation Eighth Research Institute Industry-University-Research Cooperation Fund(No.SAST 2020-019)。
文摘Aiming at the problem of relative navigation for non-cooperative rendezvous of spacecraft,this paper proposes a new angles-only navigation architecture using non-linear dynamics method. This method does not solve the problem of poor observability of angles-only navigation through orbital or attitude maneuvering,but improves the observability of angles-only navigation through capturing the non-linearity of the system in the evolution of relative motion. First,three relative dynamics models and their corresponding line-of-sight(LoS)measurement equations are introduced,including the rectilinear state relative dynamics model,the curvilinear state relative dynamics model,and the relative orbital elements(ROE)state relative dynamics model. Then,an observability analysis theory based on the Gramian matrix is introduced to determine which relative dynamics model could maximize the observability of angles-only navigation. Next,an adaptive extended Kalman filtering scheme is proposed to solve the problem that the angles-only navigation filter using the non-linear dynamics method is sensitive to measurement noises. Finally,the performances of the proposed angles-only navigation architecture are tested by means of numerical simulations,which demonstrates that the angles-only navigation filtering scheme without orbital or attitude maneuvering is completely feasible through improving the modeling of the relative dynamics and LoS measurement equations.
文摘Ultra-Wide Bandwidth(UWB)localization based on time of arrival(TOA)and angle of arrival(AOA)has attracted increasing interest owing to its high accuracy and low cost.However,existing localization methods often fail to achieve satisfactory accuracy in realistic environments due to multipath effects and non-line-of-sight(NLOS)propagation.In this paper,we propose a passive anchor assisted localization(PAAL)scheme,where the active anchor obtains TOA/AOA measurements to the agent while the passive anchors capture the signals from the active anchor and agent.The proposed method fully exploits the time-difference-of-arrival(TDOA)information from the measurements at the passive anchors to complement single-anchor joint TOA/AOA localization.The performance limits of the PAAL system are derived as a benchmark via the information inequality.Moreover,we implement the PAAL system on a low-cost UWB platform,which can achieve 20 cm localization accuracy in NLOS environments.
