Vehicle Positioning Algorithm Based on NHC/Virtual-MINS/OD

Abstract

Aiming at the problem of large navigation errors for unmanned vehicle based on MEMS inertial navigation systems (MEMS-INS, MINS) due to low output accuracy of MEMS inertial sensors, a redundant MINS is designed and a high precision positioning algorithm is proposed. In redundant MINS, the output of the redundant gyroscopes is estimated by the improved virtual gyro algorithm based on improved current statistical model and interacting multiple model Kalman filter (ICS-IMMKF). The weighted fusion algorithm is used to fuse the redundant accelerometer information, thus the virtual micro inertial navigation system (Virtual-MINS) is obtained. In the process of vehicle positioning based on virtual MINS, the centripetal acceleration is solved based on the kinematic constraints. Combined with the navigation information of Virtual-MINS and odometer (OD), the difference of centripetal acceleration and velocity are used as observation, and a Virtual-MINS/OD positioning algorithm based on kinematic nonholonomic constraints (NHC/Virtual-MINS/OD) is proposed. The simulation results show that the ICS-IMMKF algorithm can achieve high precision fusion of redundant gyro. In the test, azimuth maneuver is not constrained by the ground, therefore the ICS-IMMKF algorithm can obtain the azimuth rate with high precision. The experimental results show that the root mean square (RMS) of the East and North positioning errors, as well as the RMS of pitch, roll and azimuth estimation error based on the NHC/Virtual-MINS/ OD positioning algorithm are reduced to 34.15%, 22.65%, 55.11%, 64.93% and 41.37% of the NHC/#1-MINS/OD positioning algorithm, respectively. The method will provide a solution for high-precision positioning when GNSS fails.