State estimation of connected vehicles using a nonlinear ensemble filter  

作　　者：刘江 陈华展 蔡伯根 王剑 

机构地区：[1]School of Electronic and Information Engineering, Beijing Jiaotong University [2]Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation [3]State Key Laboratory of Rail Traffic Control and Safety (Beijing Jiaotong University)

出　　处：《Journal of Central South University》2015年第6期2406-2415,共10页中南大学学报（英文版）

基　　金：Project(4144081)supported by Beijing Natural Science Foundation,China;Projects(61403021,U1334211,61490705)supported by the National Natural Science Foundation of China;Project(2015RC015)supported by the Fundamental Research Funds for Central Universities,China;Project supported by the Foundation of Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control,China

摘　　要：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.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.

关 键 词：connected vehicles state estimation cooperative positioning nonlinear ensemble filter global navigation satellite system （GNSS） dedicated short range communication （DSRC） 

分 类 号：U495[交通运输工程—交通运输规划与管理]