Description: 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
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