A-Contrario Modeling for Robust Localization Using Raw GNSS Data

In Global Navigation Satellite System (GNSS) positioning, urban environments represent an issue particularly because of multipath and nonline-of-sight effects. The latter effects induce erroneous pseudorange observations that then should be discarded in order not to affect the estimation of the receiver position. This paper proposes a new approach for the detection of outliers in the pseudorange observations. Based on two models representing the distribution of inconsistent data (naive models), two criteria are proposed to partition the data between inliers and outliers and to estimate the location parameters. These criteria are then implemented in two localization algorithms. In addition, by considering hypotheses specific to GNSS localization, pseudorange selection and a regularization step are implemented in order to reduce the complexity and to improve the problem conditioning. Using simulated and actual datasets, the proposed algorithms are compared with popular and recent methods addressing the GNSS positioning problem. We show that the outlier detection improves the estimation of the receiver location and outperforms the classical approaches particularly when the environment is constrained.