Integrated GPS/INS systems have applications in many different fields. It has been recognized that tightly coupled GPS/INS performance is superior to loosely coupled. The tightly coupled system uses raw data of GPS...Integrated GPS/INS systems have applications in many different fields. It has been recognized that tightly coupled GPS/INS performance is superior to loosely coupled. The tightly coupled system uses raw data of GPS as measurements, such as pseudorange, carrier phase. This paper presents two antenna GPS carrier phase measurements to stabilize the azimuth of integrated system. The test results for the integrated system show an attitude accuracy of up to 0.1° (RMS) for pitch and roll, and up to 0.03°(RMS) for azimuth when baseline is 7 8m. The system by this integration can provide the position and velocity of high accuracy too.展开更多
A tightly coupled GPS （ global positioning system ）/SINS （ strap down inertial navigation system） based on a GMDH （ group method of data handling） neural network was presented to solve the problem of degraded ac...A tightly coupled GPS （ global positioning system ）/SINS （ strap down inertial navigation system） based on a GMDH （ group method of data handling） neural network was presented to solve the problem of degraded accuracy for less than four visible GPS satellites with poor signal quality. Positions and velocities of the satellites were predicted by a GMDH neural network, and the pseudo ranges and pseudo range rates received by the GPS receiver were simulated to ensure the regular op eration of the GPS/SINS Kalman filter during outages. In the mathematical simulation a tightly cou pled navigation system with a proposed approach has better navigation accuracy during GPS outages, and the anti jamming ability is strengthened for the tightly coupled navigation system.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
Accurate positioning and navigation play a vital role in vehicle-related applications,such as autonomous driving and precision agriculture.With the rapid development of Global Navigation Satellite Systems(GNSS),Precis...Accurate positioning and navigation play a vital role in vehicle-related applications,such as autonomous driving and precision agriculture.With the rapid development of Global Navigation Satellite Systems(GNSS),Precise Point Positioning(PPP)technique,as a global positioning solution,has been widely applied due to its convenient operation.Nevertheless,the performance of PPP is severely affected by signal interference,especially in GNSS-challenged environments.Inertial Navigation System(INS)aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments,but suffers from degradation during GNSS outages.LiDAR(Laser Imaging,Detection,and Ranging)-Inertial Odometry(LIO),which has performed well in local navigation,can restrain the divergence of Inertial Measurement Units(IMU).However,in long-range navigation,error accumulation is inevitable if no external aids are applied.To improve vehicle navigation performance,we proposed a tightly coupled GNSS PPP/INS/LiDAR(GIL)integration method,which tightly integrates the raw measurements from multi-GNSS PPP,Micro-Electro-Mechanical System(MEMS)-IMU,and LiDAR to achieve high-accuracy and reliable navigation in urban environments.Several experiments were conducted to evaluate this method.The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors(RMSEs)of the proposed GIL method have the improvements of 63.0%,51.3%,and 62.2%in east,north,and vertical components,respectively.The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment(i.e.,the environment with GNSS signals partly-blocked)and meter-level positioning accuracy in GNSS difficult environment(i.e.,the environment with GNSS hardly used).Besides,the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.展开更多
文摘Integrated GPS/INS systems have applications in many different fields. It has been recognized that tightly coupled GPS/INS performance is superior to loosely coupled. The tightly coupled system uses raw data of GPS as measurements, such as pseudorange, carrier phase. This paper presents two antenna GPS carrier phase measurements to stabilize the azimuth of integrated system. The test results for the integrated system show an attitude accuracy of up to 0.1° (RMS) for pitch and roll, and up to 0.03°(RMS) for azimuth when baseline is 7 8m. The system by this integration can provide the position and velocity of high accuracy too.
文摘A tightly coupled GPS （ global positioning system ）/SINS （ strap down inertial navigation system） based on a GMDH （ group method of data handling） neural network was presented to solve the problem of degraded accuracy for less than four visible GPS satellites with poor signal quality. Positions and velocities of the satellites were predicted by a GMDH neural network, and the pseudo ranges and pseudo range rates received by the GPS receiver were simulated to ensure the regular op eration of the GPS/SINS Kalman filter during outages. In the mathematical simulation a tightly cou pled navigation system with a proposed approach has better navigation accuracy during GPS outages, and the anti jamming ability is strengthened for the tightly coupled navigation system.
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the National Natural Science Foundation of China(Grant 41774030,Grant 41974027,and Grant 41974029)the Hubei Province Natural Science Foundation of China(Grant 2018CFA081)+1 种基金the frontier project of basic application from Wuhan science and technology bureau(Grant 2019010701011395)the Sino-German mobility programme(Grant No.M-0054).
文摘Accurate positioning and navigation play a vital role in vehicle-related applications,such as autonomous driving and precision agriculture.With the rapid development of Global Navigation Satellite Systems(GNSS),Precise Point Positioning(PPP)technique,as a global positioning solution,has been widely applied due to its convenient operation.Nevertheless,the performance of PPP is severely affected by signal interference,especially in GNSS-challenged environments.Inertial Navigation System(INS)aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments,but suffers from degradation during GNSS outages.LiDAR(Laser Imaging,Detection,and Ranging)-Inertial Odometry(LIO),which has performed well in local navigation,can restrain the divergence of Inertial Measurement Units(IMU).However,in long-range navigation,error accumulation is inevitable if no external aids are applied.To improve vehicle navigation performance,we proposed a tightly coupled GNSS PPP/INS/LiDAR(GIL)integration method,which tightly integrates the raw measurements from multi-GNSS PPP,Micro-Electro-Mechanical System(MEMS)-IMU,and LiDAR to achieve high-accuracy and reliable navigation in urban environments.Several experiments were conducted to evaluate this method.The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors(RMSEs)of the proposed GIL method have the improvements of 63.0%,51.3%,and 62.2%in east,north,and vertical components,respectively.The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment(i.e.,the environment with GNSS signals partly-blocked)and meter-level positioning accuracy in GNSS difficult environment(i.e.,the environment with GNSS hardly used).Besides,the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.
