Fusion Estimation Method for Vehicle Centroid Sideslip Angle Based on Adaptive Square-Root Cubature Kalman Filtering

To address the issues of computational interruptions and unknown noise statistical properties when using the Cubature Kalman Filter (CKF) method to estimate the vehicle centroid sideslip angle, this paper delivers a fusion estimation method based on Adaptive Square-Root Cubature Kalman Filter (ASRCKF). First, vehicle dynamics and kinematics models are established separately, and ASRCKF algorithms are used to design dynamic model estimators and kinematic model estimators for vehicle state estimation. On this basis, the advantages of both dynamic and kinematic model estimators are fully combined through adaptive weight dynamic adjustment to obtain more accurate estimates of the vehicle centroid sideslip angle. Simulation and real vehicle test results indicate that the designed estimation method effectively improves the precision of vehicle state estimation.