New Vehicle Positioning Method Based on RMINS/KC and Robust Adaptive KF

Abstract

Aiming at the characteristics of vehicle positioning, a new vehicle positioning method based on the rotation modulation (RM) technology, kinematic constraint (KC), and the robust adaptive Kalman filter (RAKF) is proposed. On the one hand, on the basis of RM inertial navigation system and KC, this method achieves the whitening of colored measurement noise by improving the measurement equation; on the other hand, the RAKF algorithm is used to suppress the influence of the dynamic model error and observation anomaly on the estimation of the state parameters. Finally, the proposed new method is applied to the practical vehicle positioning system. Compared with the results of KF or MEMS-RMINS, the maximum position error of the proposed method by using RAKF is less than that of the KF algorithm or MEMS-RMINS, and the positioning accuracy is improved by 26.7% than that of the KF algorithm and only 21.87% of the MEMS-RMINS. At the same time, when the system has the observation anomaly, this method can also ensure the positioning accuracy of the system. The experimental results show that the new method can effectively suppress the influence of the system model error and observation anomaly to improve the vehicle positioning accuracy.