Research on SINS/GNSS Integrated Positioning for Highway Vehicles

This study investigates the application of integrated positioning based on SINS (Strapdown Inertial Navigation System) and GNSS (Global Navigation Satellite System) for highway vehicle navigation. While GNSS offers high-precision outdoor positioning, it is susceptible to signal obstructions, whereas SINS enables autonomous positioning without external signals but accumulates drift errors over time. To enhance positioning accuracy, this study employs three nonlinear filters—Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Cubature Kalman Filter (CKF)—for multi-source data fusion. Experimental results demonstrate that EKF, UKF, and CKF achieve faster convergence, higher stability, and smoother error curves when handling nonlinear problems. Through simulation experiments and field measurements, the strengths of each algorithm are validated across different metrics and directions. Considering sensor limitations and implementation complexity, EKF outperforms other algorithms in scenarios with limited sensors. The findings of this study provide a foundation for improving vehicle navigation accuracy in complex environments.