Strapdown inertial navigation system（SINS） requires an initialization process that establishes the relationship between the body frame and the local geographic reference.This process,called alignment,is generally us...Strapdown inertial navigation system（SINS） requires an initialization process that establishes the relationship between the body frame and the local geographic reference.This process,called alignment,is generally used to estimate the initial attitude angles.This is possible because an accurate determination of the inertial measurement unit（IMU） motion is available based on the measurement obtained from global position system（GPS）.But the update frequency of GPS is much lower than SINS.Due to the non-synchronous data streams from GPS and SINS,the initial attitude angles may not be computed accurately enough.In addition,the estimated initial attitude angles may have relatively large uncertainties that can affect the accuracy of other navigation parameters.This paper presents an effective approach of matching the velocities which are provided by GPS and SINS.In this approach,a digital high-pass filter,which implements a pre-filtering scheme of the measured signal,is used to filter the Schuler cycle of discrete velocity difference between the SINS and GPS.Simulation results show that this approach improves the accuracy greatly and makes the convergence time satisfy the required accuracy.展开更多
Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased es...Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.展开更多
In inertial navigation system(INS) and global positioning system(GPS) integrated system, GPS antennas are usually not located at the same location as the inertial measurement unit(IMU) of the INS, so the lever arm eff...In inertial navigation system(INS) and global positioning system(GPS) integrated system, GPS antennas are usually not located at the same location as the inertial measurement unit(IMU) of the INS, so the lever arm effect exists, which makes the observation equation highly nonlinear. The INS/GPS integration with constant lever arm effect is studied. The position relation of IMU and GPS's antenna is represented in the earth centered earth fixed frame, while the velocity relation of these two systems is represented in local horizontal frame. Due to the small integration time interval of INS, i.e. 0.1 s in this work, the nonlinearity in the INS error equation is trivial, so the linear INS error model is constructed and addressed by Kalman filter's prediction step. On the other hand, the high nonlinearity in the observation equation due to lever arm effect is addressed by unscented Kalman filter's update step to attain higher accuracy and better applicability. Simulation is designed and the performance of the hybrid filter is validated.展开更多
在典型的城市环境中,单一GPS定位模式已很难满足连续定位的要求,采用GPS和其他非LOS(Line of Sight,视线矢量)定位方法(如RFID定位方法)进行组合可实现弱信号或无导航信号环境下的定位。针对GPS/RFID组合定位系统中全选RFID观测值所带...在典型的城市环境中,单一GPS定位模式已很难满足连续定位的要求,采用GPS和其他非LOS(Line of Sight,视线矢量)定位方法(如RFID定位方法)进行组合可实现弱信号或无导航信号环境下的定位。针对GPS/RFID组合定位系统中全选RFID观测值所带来的较高的计算复杂度的问题,提出了一种在加权观测值条件下使组合定位误差最小的RFID观测值优选方法。实测实验结果表明,采用这种优选RFID观测值的GPS/RFID组合定位系统可改善在GPS信号受遮挡条件下的GDOP值,有效提高单一卫星导航定位系统的定位可用性和定位精度,并大大降低了系统的计算复杂度。展开更多
车载定位导航系统(Vehicle Location and Navigation System,VLNS)综合了多种高科技技术,旨在改善城市交通状况,给驾驶者一个方便快捷的路径诱导。而车载定位技术是车载导航系统的一个关键技术。通过市场调研剖析了车载定位导航系统的现...车载定位导航系统(Vehicle Location and Navigation System,VLNS)综合了多种高科技技术,旨在改善城市交通状况,给驾驶者一个方便快捷的路径诱导。而车载定位技术是车载导航系统的一个关键技术。通过市场调研剖析了车载定位导航系统的现状,并对GPS/DR组合定位系统进行了跑车实验,给出了相应结论。展开更多
基金supported by the National Natural Science Foundation of China (6083400560775001)
文摘Strapdown inertial navigation system（SINS） requires an initialization process that establishes the relationship between the body frame and the local geographic reference.This process,called alignment,is generally used to estimate the initial attitude angles.This is possible because an accurate determination of the inertial measurement unit（IMU） motion is available based on the measurement obtained from global position system（GPS）.But the update frequency of GPS is much lower than SINS.Due to the non-synchronous data streams from GPS and SINS,the initial attitude angles may not be computed accurately enough.In addition,the estimated initial attitude angles may have relatively large uncertainties that can affect the accuracy of other navigation parameters.This paper presents an effective approach of matching the velocities which are provided by GPS and SINS.In this approach,a digital high-pass filter,which implements a pre-filtering scheme of the measured signal,is used to filter the Schuler cycle of discrete velocity difference between the SINS and GPS.Simulation results show that this approach improves the accuracy greatly and makes the convergence time satisfy the required accuracy.
基金supported by the Fundamental Research Funds for the Central Universities(xzy022020045)the National Natural Science Foundation of China(61976175)。
文摘Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.
基金Project(41374018)supported by the National Natural Science Foundation of ChinaProject(J13LN74)supported by the Shandong Province Higher Educational Science and Technology Program,China
文摘In inertial navigation system(INS) and global positioning system(GPS) integrated system, GPS antennas are usually not located at the same location as the inertial measurement unit(IMU) of the INS, so the lever arm effect exists, which makes the observation equation highly nonlinear. The INS/GPS integration with constant lever arm effect is studied. The position relation of IMU and GPS's antenna is represented in the earth centered earth fixed frame, while the velocity relation of these two systems is represented in local horizontal frame. Due to the small integration time interval of INS, i.e. 0.1 s in this work, the nonlinearity in the INS error equation is trivial, so the linear INS error model is constructed and addressed by Kalman filter's prediction step. On the other hand, the high nonlinearity in the observation equation due to lever arm effect is addressed by unscented Kalman filter's update step to attain higher accuracy and better applicability. Simulation is designed and the performance of the hybrid filter is validated.
文摘在典型的城市环境中,单一GPS定位模式已很难满足连续定位的要求,采用GPS和其他非LOS(Line of Sight,视线矢量)定位方法(如RFID定位方法)进行组合可实现弱信号或无导航信号环境下的定位。针对GPS/RFID组合定位系统中全选RFID观测值所带来的较高的计算复杂度的问题,提出了一种在加权观测值条件下使组合定位误差最小的RFID观测值优选方法。实测实验结果表明,采用这种优选RFID观测值的GPS/RFID组合定位系统可改善在GPS信号受遮挡条件下的GDOP值,有效提高单一卫星导航定位系统的定位可用性和定位精度,并大大降低了系统的计算复杂度。
文摘车载定位导航系统(Vehicle Location and Navigation System,VLNS)综合了多种高科技技术,旨在改善城市交通状况,给驾驶者一个方便快捷的路径诱导。而车载定位技术是车载导航系统的一个关键技术。通过市场调研剖析了车载定位导航系统的现状,并对GPS/DR组合定位系统进行了跑车实验,给出了相应结论。
