Determination of Pseudorange Error Models and Multipath Characterization under Signal-Degraded Scenarios

As Global Navigation Satellite Systems (GNSS) become increasingly important for the application of autonomous transportation, the characterization of the uncertainty present in GNSS signals has been a recurrent topic in the GNSS community. With multipath being the most pronounced source of error while navigating during challenging scenarios, characterizing this process is a growing requirement. Improper modeling of the measurements variances can reduce the accuracy of the estimation as much as a gross bias on these measurements. It is therefore required to have reliable and statistically meaningful modeling of errors. To isolate the multipath from the other error sources, the dual-frequency iono-corrected code-minus-carrier combination (CMC) is employed. Conjugate prior Bayesian analysis is used to characterize the variance of the multipath effect to later regress the unknown parameter of the proposed error models. For the derivation of these models, massive amounts of multi-frequency GPS and Galileo data was collected. The comparison between the proposed models was carried out with actual data from a challenging signal-degraded scenario, in terms of positioning and integrity monitoring performance.