Improved positioning algorithm based on vehicle-mounted GNSS and IMU positioning system
To solve the problem of data fusion noise errors caused by changes in actual road conditions in traditional positioning algorithms,the motion data collected by on-board sensors was adapted as a threshold condition for adaptive filtering and dynamic switching to enhance the robustness and adaptability of the integrated positioning system which combined vehicle-mounted global navigation satellite system(GNSS)with inertial measurement unit(IMU)positioning system.An improved adaptive dynamic positioning algorithm was proposed,combining the extended Kalman filter(EKF)with the unscented Kalman filter(UKF)based on real-time motion models,as a way to suppress the error disturbance generated by the traditional single algorithm when the vehicle motion model changed.Compared to the EKF and UKF,the root mean square error(RMSE)of the improved algorithm was reduced by 75.26％and 58.48％respectively.The average distance error of the improved algorithm in the actual in-vehicle scenario beneath an overpass was 2.32 cm,an improvement of roughly 61.65％over the performance before the change.These results demonstrated the efficacy of the improved algorithm in the complex urban traffic.
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