差分全球定位系统(difference global positioning system,DGPS)与惯性导航系统(inertial navigation system,INS)所构成的组合定位测姿系统已广泛应用于高精度移动测量领域,但由于需要基准站支持,该系统作业范围有限、作业复杂且成本...差分全球定位系统(difference global positioning system,DGPS)与惯性导航系统(inertial navigation system,INS)所构成的组合定位测姿系统已广泛应用于高精度移动测量领域,但由于需要基准站支持,该系统作业范围有限、作业复杂且成本高。模糊度为浮点解的精密单点定位(precise point positioning,PPP)与INS所构成的组合系统,虽不需要架设基准站,但定位精度有限且收敛时间较长,其原因就在于模糊度为浮点解。针对以上问题,提出将模糊度为固定解的PPP与INS进行紧组合,给出了该新组合详细的观测模型和系统模型。实测车载组合导航实验对新组合进行了验证,结果表明,仅用单台GPS接收机,只需约10余分钟就能获取首次固定解;一旦实现固定,新组合的位置误差迅速由分米级降低到稳定的厘米级。展开更多
This paper describes a robust integrated positioning method to provide ground vehicles in urban environments with accurate and reliable localization results. The localization problem is formulated as a maximum a poste...This paper describes a robust integrated positioning method to provide ground vehicles in urban environments with accurate and reliable localization results. The localization problem is formulated as a maximum a posteriori probability estimation and solved using graph optimization instead of Bayesian filter. Graph optimization exploits the inherent sparsity of the observation process to satisfy the real-time requirement and only updates the incremental portion of the variables with each new incoming measurement. Unlike the Extended Kalman Filter （EKF） in a typical tightly coupled Global Navigation Satellite System/Inertial Navigation System （GNSS/INS） integrated system, optimization iterates the solution for the entire trajectory. Thus, previous INS measurements may provide redundant motion constraints for satellite fault detection. With the help of data redundancy, we add a new variable that presents reliability of GNSS measurement to the original state vector for adjusting the weight of corresponding pseudorange residual and exclude faulty measurements. The proposed method is demonstrated on datasets with artificial noise, simulating a moving vehicle equipped with GNSS receiver and inertial measurement unit. Compared with the solutions obtained by the EKF with innovation filtering, the new reliability factor can indicate the satellite faults effectively and provide successful positioning despite contaminated observations.展开更多
针对精密单点定位(Precise Point Positioning,PPP)动态定位精度低、收敛速度慢等问题,本文采用PPP/INS紧组合系统来达到改善PPP动态定位性能的目的。本文对PPP/INS紧组合的观测方程、误差补偿模型、参数估计模型等进行详细推导。通过...针对精密单点定位(Precise Point Positioning,PPP)动态定位精度低、收敛速度慢等问题,本文采用PPP/INS紧组合系统来达到改善PPP动态定位性能的目的。本文对PPP/INS紧组合的观测方程、误差补偿模型、参数估计模型等进行详细推导。通过车载实验采集的卫星观测数据和不同等级的惯性数据,对动态PPP及PPP/INS紧组合的定位定姿性能进行分析,评估不同等级惯性传感器对PPP/INS紧组合定位精度和收敛速度的影响。实验结果表明:PPP/INS紧组合在北-东-高方向的位置RMS相对于PPP分别改善了70.2%、29.1%和16.8%,达到4.8 cm、12.3 cm和7.4 cm。在卫星跟踪条件良好时,惯性传感器性能对PPP/INS紧组合定位精度影响不大;而在卫星观测条件不足时,惯性传感器性能对PPP/INS紧组合定位精度影响明显。此外,仿真和恶劣条件下的数据结果表明,PPP/INS初始定位精度与收敛速度随惯性传感器性能提高而改善明显。展开更多
文摘差分全球定位系统(difference global positioning system,DGPS)与惯性导航系统(inertial navigation system,INS)所构成的组合定位测姿系统已广泛应用于高精度移动测量领域,但由于需要基准站支持,该系统作业范围有限、作业复杂且成本高。模糊度为浮点解的精密单点定位(precise point positioning,PPP)与INS所构成的组合系统,虽不需要架设基准站,但定位精度有限且收敛时间较长,其原因就在于模糊度为浮点解。针对以上问题,提出将模糊度为固定解的PPP与INS进行紧组合,给出了该新组合详细的观测模型和系统模型。实测车载组合导航实验对新组合进行了验证,结果表明,仅用单台GPS接收机,只需约10余分钟就能获取首次固定解;一旦实现固定,新组合的位置误差迅速由分米级降低到稳定的厘米级。
文摘This paper describes a robust integrated positioning method to provide ground vehicles in urban environments with accurate and reliable localization results. The localization problem is formulated as a maximum a posteriori probability estimation and solved using graph optimization instead of Bayesian filter. Graph optimization exploits the inherent sparsity of the observation process to satisfy the real-time requirement and only updates the incremental portion of the variables with each new incoming measurement. Unlike the Extended Kalman Filter （EKF） in a typical tightly coupled Global Navigation Satellite System/Inertial Navigation System （GNSS/INS） integrated system, optimization iterates the solution for the entire trajectory. Thus, previous INS measurements may provide redundant motion constraints for satellite fault detection. With the help of data redundancy, we add a new variable that presents reliability of GNSS measurement to the original state vector for adjusting the weight of corresponding pseudorange residual and exclude faulty measurements. The proposed method is demonstrated on datasets with artificial noise, simulating a moving vehicle equipped with GNSS receiver and inertial measurement unit. Compared with the solutions obtained by the EKF with innovation filtering, the new reliability factor can indicate the satellite faults effectively and provide successful positioning despite contaminated observations.
