Combining the observation data from five Multi-GNSS Experiment(MGEX)stations with the precise orbit and clock products from Global Positioning System(GPS)and BeiDou Navigation Satellite System(BDS),we studied the mode...Combining the observation data from five Multi-GNSS Experiment(MGEX)stations with the precise orbit and clock products from Global Positioning System(GPS)and BeiDou Navigation Satellite System(BDS),we studied the model of combined GPS/BDS precise point positioning,and then analyzed the convergence speed and short-time(6 h)positioning accuracy.The calculation results show that in static positioning,the average convergence time of GPS is about 50 min,and its horizontal accuracy is better than 2 cm while the vertical accuracy is better than 4 cm.The convergence speed of combined GPS/BDS is about 40 min,and its positioning accuracy is close to that of GPS.In kinematic positioning,the average convergence time of GPS is about 72 min,and its horizontal accuracy is better than 5 cm while the vertical accuracy is better than 12 cm.The average convergence time of GPS/BDS is about 57 min,and its horizontal accuracy is better than 3 cm while the vertical accuracy is better than 9 cm.Combined GPS/BDS has significantly improved the convergence speed,and its positioning accuracy is slightly than that of GPS.展开更多
When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the feature...When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the features of the dual-mode observation.Due to multipath effect,positioning accuracy of present Kalman filter algorithm is really low.To solve this problem,a chaotic immune-vaccine particle swarm optimization_extended Kalman filter(CIPSO_EKF)algorithm is proposed to improve the output accuracy of the Kalman filter.By chaotic mapping and immunization,the particle swarm algorithm is first optimized,and then the optimized particle swarm algorithm is used to optimize the observation error covariance matrix.The optimal parameters are provided to the EKF,which can effectively reduce the impact of the observation value oscillation caused by multipath effect on positioning accuracy.At the same time,the train positioning results of EKF and CIPSO_EKF algorithms are compared.The eastward position errors and velocity errors show that CIPSO_EKF algorithm has faster convergence speed and higher real-time performance,which can effectively suppress interference and improve positioning accuracy.展开更多
抗差Kalman滤波是控制GNSS动态导航定位中观测异常的有效算法,当应用到GPS/BDS实时动态精密单点定位(Precise Point Positioning,PPP)时,会出现某些历元定位精度甚至不如单一系统定位精度高,这主要是因为同一接收机接收的不同种类卫星...抗差Kalman滤波是控制GNSS动态导航定位中观测异常的有效算法,当应用到GPS/BDS实时动态精密单点定位(Precise Point Positioning,PPP)时,会出现某些历元定位精度甚至不如单一系统定位精度高,这主要是因为同一接收机接收的不同种类卫星观测量的随机特性不同,使得观测量验后残差的分布特性不一致,抗差估计时随机特性不同的观测量验后残差互比,反而对某一系统优质数据也进行了降权,导致定位结果出现偏差,减弱了GPS/BDS融合精密单点定位的优势。针对这一问题,提出了卫星分群的抗差Kalman滤波算法,并应用到GPS/BDS融合精密单点定位中,算法的核心是在每一历元观测数据质量控制时根据卫星类型分类构建方差膨胀因子,给出了算法的实施步骤,最后通过MGEX实测数据进行了验证,结果表明算法应用到GPS/BDS融合精密单点定位中,相较传统的抗差Kalman滤波算法在东、北、天三个方向分别提高了34.6%、33.3%、31.0%,同时表明该算法提高了GPS/BDS融合精密单点定位的可靠性。展开更多
基金supported by Director Foundation of the Institute of Seismology,China Earthquake Administration(6110).
文摘Combining the observation data from five Multi-GNSS Experiment(MGEX)stations with the precise orbit and clock products from Global Positioning System(GPS)and BeiDou Navigation Satellite System(BDS),we studied the model of combined GPS/BDS precise point positioning,and then analyzed the convergence speed and short-time(6 h)positioning accuracy.The calculation results show that in static positioning,the average convergence time of GPS is about 50 min,and its horizontal accuracy is better than 2 cm while the vertical accuracy is better than 4 cm.The convergence speed of combined GPS/BDS is about 40 min,and its positioning accuracy is close to that of GPS.In kinematic positioning,the average convergence time of GPS is about 72 min,and its horizontal accuracy is better than 5 cm while the vertical accuracy is better than 12 cm.The average convergence time of GPS/BDS is about 57 min,and its horizontal accuracy is better than 3 cm while the vertical accuracy is better than 9 cm.Combined GPS/BDS has significantly improved the convergence speed,and its positioning accuracy is slightly than that of GPS.
基金National Natural Science Foundation of China(Nos.61662070,61363059)Youth Science Fund Project of Lanzhou Jiaotong University(No.2018036)。
文摘When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the features of the dual-mode observation.Due to multipath effect,positioning accuracy of present Kalman filter algorithm is really low.To solve this problem,a chaotic immune-vaccine particle swarm optimization_extended Kalman filter(CIPSO_EKF)algorithm is proposed to improve the output accuracy of the Kalman filter.By chaotic mapping and immunization,the particle swarm algorithm is first optimized,and then the optimized particle swarm algorithm is used to optimize the observation error covariance matrix.The optimal parameters are provided to the EKF,which can effectively reduce the impact of the observation value oscillation caused by multipath effect on positioning accuracy.At the same time,the train positioning results of EKF and CIPSO_EKF algorithms are compared.The eastward position errors and velocity errors show that CIPSO_EKF algorithm has faster convergence speed and higher real-time performance,which can effectively suppress interference and improve positioning accuracy.
文摘抗差Kalman滤波是控制GNSS动态导航定位中观测异常的有效算法,当应用到GPS/BDS实时动态精密单点定位(Precise Point Positioning,PPP)时,会出现某些历元定位精度甚至不如单一系统定位精度高,这主要是因为同一接收机接收的不同种类卫星观测量的随机特性不同,使得观测量验后残差的分布特性不一致,抗差估计时随机特性不同的观测量验后残差互比,反而对某一系统优质数据也进行了降权,导致定位结果出现偏差,减弱了GPS/BDS融合精密单点定位的优势。针对这一问题,提出了卫星分群的抗差Kalman滤波算法,并应用到GPS/BDS融合精密单点定位中,算法的核心是在每一历元观测数据质量控制时根据卫星类型分类构建方差膨胀因子,给出了算法的实施步骤,最后通过MGEX实测数据进行了验证,结果表明算法应用到GPS/BDS融合精密单点定位中,相较传统的抗差Kalman滤波算法在东、北、天三个方向分别提高了34.6%、33.3%、31.0%,同时表明该算法提高了GPS/BDS融合精密单点定位的可靠性。
