Routing algorithms in satellite constellation networks usually make use of the local state information to adapt to the topology and traffic dynamics,since it’s difficult to obtain the global states in time due to the...Routing algorithms in satellite constellation networks usually make use of the local state information to adapt to the topology and traffic dynamics,since it’s difficult to obtain the global states in time due to the spatial large-scale feature of constellation networks.Furthermore,they use different range of local states and give these states distinct weights.However,the behind design criterion is ambiguous and often based on experience.This paper discusses the problem from the perspective of complex network.A universal local-state routing model with tunable parameters is presented to generalize the common characteristics of local-state routing algorithms for satellite constellation networks.Based on this,the impacts of localstate routing algorithms on performance and the correlation between routing and traffic dynamics are analyzed in detail.Among them,the tunable parameters,the congestion propagation process,the critical packet sending rate,and the network robustness are discussed respectively.Experimental results show that routing algorithms can achieve a satisfactory performance by maintaining a limited state awareness capability and obtaining the states in a range below the average path length.This provides a valuable design basis for routing algorithms in satellite constellation networks.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
The concept of connected vehicles is with great potentials for enhancing the road transportation systems in the future. To support the functions and applications under the connected vehicles frame, the estimation of d...The concept of connected vehicles is with great potentials for enhancing the road transportation systems in the future. To support the functions and applications under the connected vehicles frame, the estimation of dynamic states of the vehicles under the cooperative environments is a fundamental issue. By integrating multiple sensors, localization modules in OBUs(on-board units) require effective estimation solutions to cope with various operation conditions. Based on the filtering estimation framework for sensor fusion, an ensemble Kalman filter(En KF) is introduced to estimate the vehicle's state with observations from navigation satellites and neighborhood vehicles, and the original En KF solution is improved by using the cubature transformation to fulfill the requirements of the nonlinearity approximation capability, where the conventional ensemble analysis operation in En KF is modified to enhance the estimation performance without increasing the computational burden significantly. Simulation results from a nonlinear case and the cooperative vehicle localization scenario illustrate the capability of the proposed filter, which is crucial to realize the active safety of connected vehicles in future intelligent transportation.展开更多
The drag-free satellites are widely used in the field of fundamental science as they enable the high-precision measurement in pure gravity fields. This paper investigates the estimation of local orbital reference fram...The drag-free satellites are widely used in the field of fundamental science as they enable the high-precision measurement in pure gravity fields. This paper investigates the estimation of local orbital reference frame(LORF) for drag-free satellites. An approach, taking account of the combination of the minimum estimation error and power spectral density(PSD) constraint in frequency domain, is proposed. Firstly, the relationship between eigenvalues of estimator and transfer function is built to analyze the suppression and amplification effect on input signals and obtain the eigenvalue range. Secondly, an optimization model for state estimator design with minimum estimation error in time domain and PSD constraint in frequency domain is established. It is solved by the sequential quadratic programming(SQP) algorithm. Finally, the orbital reference frame estimation of low-earth-orbit satellite is taken as an example, and the estimator of minimum variance with PSD constraint is designed and analyzed using the method proposed in this paper.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62171466and the National Natural Science Foundation of China under Grant 61971440+1 种基金the National Key R&D Program of China under Grant 2018YFB1801103the Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu under Grant BK20192002。
文摘Routing algorithms in satellite constellation networks usually make use of the local state information to adapt to the topology and traffic dynamics,since it’s difficult to obtain the global states in time due to the spatial large-scale feature of constellation networks.Furthermore,they use different range of local states and give these states distinct weights.However,the behind design criterion is ambiguous and often based on experience.This paper discusses the problem from the perspective of complex network.A universal local-state routing model with tunable parameters is presented to generalize the common characteristics of local-state routing algorithms for satellite constellation networks.Based on this,the impacts of localstate routing algorithms on performance and the correlation between routing and traffic dynamics are analyzed in detail.Among them,the tunable parameters,the congestion propagation process,the critical packet sending rate,and the network robustness are discussed respectively.Experimental results show that routing algorithms can achieve a satisfactory performance by maintaining a limited state awareness capability and obtaining the states in a range below the average path length.This provides a valuable design basis for routing algorithms in satellite constellation networks.
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Project(4144081)supported by Beijing Natural Science Foundation,ChinaProjects(61403021,U1334211,61490705)supported by the National Natural Science Foundation of China+1 种基金Project(2015RC015)supported by the Fundamental Research Funds for Central Universities,ChinaProject supported by the Foundation of Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control,China
文摘The concept of connected vehicles is with great potentials for enhancing the road transportation systems in the future. To support the functions and applications under the connected vehicles frame, the estimation of dynamic states of the vehicles under the cooperative environments is a fundamental issue. By integrating multiple sensors, localization modules in OBUs(on-board units) require effective estimation solutions to cope with various operation conditions. Based on the filtering estimation framework for sensor fusion, an ensemble Kalman filter(En KF) is introduced to estimate the vehicle's state with observations from navigation satellites and neighborhood vehicles, and the original En KF solution is improved by using the cubature transformation to fulfill the requirements of the nonlinearity approximation capability, where the conventional ensemble analysis operation in En KF is modified to enhance the estimation performance without increasing the computational burden significantly. Simulation results from a nonlinear case and the cooperative vehicle localization scenario illustrate the capability of the proposed filter, which is crucial to realize the active safety of connected vehicles in future intelligent transportation.
基金co-supported by the Open Fund of Joint Key Laboratory of Microsatellite of CAS (No. KFKT15SYS1)the Innovation Foundation of CAS (No. CXJJ-14-Q52)
文摘The drag-free satellites are widely used in the field of fundamental science as they enable the high-precision measurement in pure gravity fields. This paper investigates the estimation of local orbital reference frame(LORF) for drag-free satellites. An approach, taking account of the combination of the minimum estimation error and power spectral density(PSD) constraint in frequency domain, is proposed. Firstly, the relationship between eigenvalues of estimator and transfer function is built to analyze the suppression and amplification effect on input signals and obtain the eigenvalue range. Secondly, an optimization model for state estimator design with minimum estimation error in time domain and PSD constraint in frequency domain is established. It is solved by the sequential quadratic programming(SQP) algorithm. Finally, the orbital reference frame estimation of low-earth-orbit satellite is taken as an example, and the estimator of minimum variance with PSD constraint is designed and analyzed using the method proposed in this paper.
