An Outlier Detection Method of GNSS/SINS Integrated Navigation Based on Accelerometer Bias Stability

In the ground vehicle integrated navigation, GNSS observations are often interfered by complex ground environment and thus, its positioning result is more prone to contain outliers which can seriously affect GNSS/SINS integrated filter solution. This paper, from the perspective of IMU system error feature, studies an outlier detection method of GNSS/SINS integrated navigation based on accelerometer bias stability. According to the outlier of accelerometer bias result, the method detects gross errors in GNSS position, velocity, etc; and then applies the robust strategies of rejection and weight reduction to resist the influence of gross errors. The method is analyzed by a set of vehicle measured data. The results show that the observation outliers can greatly affect the accelerometer bias result and thus taking the accelerometer bias stability as a condition, the outliers can be exactly detected. In every direction of ENU, the RMS of position and the RMS of velocity are improved by 70.8% and 87.9% respectively; the RMS of attitude is improved by 77.7%. The method greatly improves the accuracy and robustness of integrated navigation results and provides a new strategy for robust processing of integrated navigation data. © 2018, Research and Development Office of Wuhan University. All right reserved.