GNSS bias correction for localization systems

This paper deals with using of low-cost Global Navigation Satellite System (GNSS) sensors in a localization process for an autonomous guidance system of mobile robots. Generally, this process is made using a Kalman Filter (KF) to fuse information coming from different sensors. But as GNSS error is an unpredictable stochastiscal process, the localization estimated by the KF becomes unreliable. To solve this problem, the error of a cheap GNSS receiver is analyzed. Then, an AutoRegressive process (AR process) is used to establish a reliable prediction model. A second problem of GNSS systems concerns the disturbances in the observation of satellites. To detect this disturbance, we use a condition based on the Mahalanobis distance. This model and condition are taken into account in the localization system to improve its accuracy and its reliability. To finish, many experimental results show the improvement of GNSS localization system using our modeling and disturbance detection process.