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 ever-changing GPS conditions. The performance of the entire system is verified through experimental results using a commercial vehicle. Finally, through a comparative study, the effectiveness of the proposed algorithm is confirmed.
Vehicle Positioning Based on Velocity and Heading Angle Observer Using Low-Cost Sensor Fusion
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 3, 2017; final manuscript received May 10, 2017; published online August 10, 2017. Assoc. Editor: Zongxuan Sun.
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Park, G., Hwang, Y., and Choi, S. B. (August 10, 2017). "Vehicle Positioning Based on Velocity and Heading Angle Observer Using Low-Cost Sensor Fusion." ASME. J. Dyn. Sys., Meas., Control. December 2017; 139(12): 121008. https://doi.org/10.1115/1.4036881
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