Vehicle positioning based on velocity and heading angle observer using low-cost sensor fusion

The vehicle positioning system can be utilized for various automotive applications. Primarily focusing on practicality, this paper presents a new method for vehicle positioning systems using low-cost sensor fusion, which combines global positioning system (GPS) data and data from easily available in-vehicle sensors. As part of the vehicle positioning, a novel nonlinear observer for vehicle velocity and heading angle estimation is designed, and the convergence of estimation error is also investigated using Lyapunov stability analysis. Based on this estimation information, a new adaptive Kalman filter with rule-based logic provides robust and highly accurate estimations of the vehicle position. It adjusts the noise covariance matrices Q and R in order to adapt to various environments, such as different driving maneuvers and everchanging GPS conditions. The performance of the entire system is verified through experimental results 1 Corresponding author. Journal of Dynamic Systems, Measurement, and Control DS-17-1002, Choi, 2 using a commercial vehicle. Finally, through a comparative study, the effectiveness of the proposed algorithm is confirmed.