An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） s...An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the performance.In this research, an Adaptive Interacting Multiple Model（AIMM） filter is developed to enhance performance. The soft-switching property of Interacting Multiple Model（IMM） algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System（GPS） and Bei Dou Navigation Satellite System（BDS） were post-processed in differential mode.The results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpartfor GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.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
With the support of the national nature science foundation,the Academy of Space Electronic Information Technology is developing a novel compact spaceborne GNSS receiver,referred to as the HiSGR(High Sensitivity GNSS R...With the support of the national nature science foundation,the Academy of Space Electronic Information Technology is developing a novel compact spaceborne GNSS receiver,referred to as the HiSGR(High Sensitivity GNSS Receiver).This receiver can operate effectively in the full range of Earth orbiting missions,from LEO(Low Earth Orbit)to geostationary and beyond.Improved signal detection algorithms are used in the signal process section of the HiSGR and an inertial sensor is used for GNSS/INS ultra tight coupled design,which makes the acquisition process fast and provides improved tracking performance for weaker GPS signals in the presence of high dynamics.Extensive tests are performed using the HiSGR to demonstrate the good performance of some crucial specifications,by employing a real GNSS signal received in an open field and through hardware-in-the-loop simulation.Receiver performance is demonstrated for LEO and GEO scenarios.A ground vehicle running test is performed for demonstration of fast acquisition and reacquisition capabilities under conditions of signal loss.The HiSGR showed good performance and it was stable during the simulations and tests,which proved its capability for future space applications.展开更多
基金co-supported by the National Key Research and Development Program of China(No.2016YFC0803103)Beijing Advanced Innovation Center for Future Urban Design(No.UDC2016050100)Beijing Postdoctoral Research Foundation
文摘An Extended Kalman Filter（EKF） is commonly used to fuse raw Global Navigation Satellite System（GNSS） measurements and Inertial Navigation System（INS） derived measurements. However, the Conventional EKF（CEKF） suffers the problem for which the uncertainty of the statistical properties to dynamic and measurement models will degrade the performance.In this research, an Adaptive Interacting Multiple Model（AIMM） filter is developed to enhance performance. The soft-switching property of Interacting Multiple Model（IMM） algorithm allows the adaptation between two levels of process noise, namely lower and upper bounds of the process noise. In particular, the Sage adaptive filtering is applied to adapt the measurement covariance on line. In addition, a classified measurement update strategy is utilized, which updates the pseudorange and Doppler observations sequentially. A field experiment was conducted to validate the proposed algorithm, the pseudorange and Doppler observations from Global Positioning System（GPS） and Bei Dou Navigation Satellite System（BDS） were post-processed in differential mode.The results indicate that decimeter-level positioning accuracy is achievable with AIMM for GPS/INS and GPS/BDS/INS configurations, and the position accuracy is improved by 35.8%, 34.3% and 33.9% for north, east and height components, respectively, compared to the CEKF counterpartfor GPS/BDS/INS. Degraded performance for BDS/INS is obtained due to the lower precision of BDS pseudorange observations.
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.
基金supported by the National Natural Science Foundation of China(No.91438107).
文摘With the support of the national nature science foundation,the Academy of Space Electronic Information Technology is developing a novel compact spaceborne GNSS receiver,referred to as the HiSGR(High Sensitivity GNSS Receiver).This receiver can operate effectively in the full range of Earth orbiting missions,from LEO(Low Earth Orbit)to geostationary and beyond.Improved signal detection algorithms are used in the signal process section of the HiSGR and an inertial sensor is used for GNSS/INS ultra tight coupled design,which makes the acquisition process fast and provides improved tracking performance for weaker GPS signals in the presence of high dynamics.Extensive tests are performed using the HiSGR to demonstrate the good performance of some crucial specifications,by employing a real GNSS signal received in an open field and through hardware-in-the-loop simulation.Receiver performance is demonstrated for LEO and GEO scenarios.A ground vehicle running test is performed for demonstration of fast acquisition and reacquisition capabilities under conditions of signal loss.The HiSGR showed good performance and it was stable during the simulations and tests,which proved its capability for future space applications.
