Vehicle sideslip angle estimation by fusing inertial measurement unit and global navigation satellite system with heading alignment

Estimation of the sideslip angle is significant for vehicle safety control systems such as electronic stability control. This paper proposes a vehicle-kinematic-model-based sideslip angle estimation method by fusing the information from an inertial measurement unit (IMU) and global navigation satellite system (GNSS) with aligning the heading from the GNSS. To estimate the velocity and attitude errors of the reduced inertial navigation system (R-INS), we first formulate the associated system error dynamics. Then, to further improve the heading estimation accuracy of the R-INS, the heading from the GNSS is aligned to the vehicle longitudinal direction by a robust regression method and adopted to estimate the heading error of the R-INS. Next, an adaptive Kalman filter is applied to estimate the errors in the R-INS to attenuate the noise influence. With the velocity in navigation coordinates and the attitude between the navigation coordinates and vehicle body coordinates from the R-INS, the velocity and sideslip angle in the vehicle body coordinates are computed. Finally, tests in straight line, double lane change (DLC), and slalom maneuvers are per-formed to verify the sideslip angle estimation and the heading alignment method. After aligning the heading from the GNSS, the sideslip angle estimation accuracy is improved, and the mean error under typical DLC and slalom maneuvers are below 0.21 degrees. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a vehicle-kinematic-model-based sideslip angle estimation method which fuses information from IMU and GNSS, aligning the heading from GNSS to improve accuracy. Through various tests, the effectiveness of the method in improving sideslip angle estimation accuracy is demonstrated.