Evidential framework for robust localization using raw GNSS data

Global Navigation Satellite Systems (GNSS) positioning in constrained environments suffers from the presence of Non Line Of Sight and multipath receptions so that, in addition to contain imprecise measurements (e.g. due to atmospheric effects), the set of GNSS pseudo-range observations includes some outliers. In this study, we evaluate the interest of the belief function framework as an alternative to the Interval Analysis approach classically used in localization to deal with imprecise data and possibly outliers. Following the basic idea of the RANSAC (RANdom SAmpling Consensus) algorithm, we propose a new detection of the outliers based on an evidential measure of the consistency of the solution. Each pseudo-range (PR) observation generates a 2D basic belief assignment (bba) that quantifies, for any 2D set, the possibility (according to the observed PR) that it includes the GNSS receiver. Outlier detection is then performed by evaluating directly the consistency of subsets of bbas and the inlier PR information is aggregated through the combination of corresponding bbas. In the case of a dynamic receiver, filtering is performed by combining the bbas derived from the new observations to a bba predicted from estimation at previous time step. Proposed approach was evaluated on two actual datasets acquired in urban environment. Results are evaluated both in terms of precision of the localization and in terms of guarantee of the solution. They compared with former approaches either in belief function framework or using interval analysis, stating the interest of the proposed algorithm.