Rectification of GNSS‑based collaborative positioning using 3D building models in urban areas

Abstract

GNSS collaborative positioning receives great attention because of the rapid development of vehicle-to-vehicle communication. Its current bottleneck is in urban areas. During the relative positioning using GNSS double-difference pseudorange measurements, the multipath effects and non-line-of-sight (NLOS) reception cannot be eliminated, or even worse, both might be aggregated. It has been widely demonstrated that 3D map aided GNSS can mitigate or even correct the multipath and NLOS effects. We, therefore, investigate the potential of aiding GNSS collaborative positioning using 3D city models. These models are used in two phases. First, the building models are used to exclude NLOS measurements at a single receiver using GNSS shadow matching positioning. Second, the models are used together with broadcast ephemeris data to generate a predicted GNSS positioning error map. Based on this error map, each receiver will be identified as experiencing healthy or degraded conditions. The receiver experiencing degraded condition will be improved by the receiver experiencing the healthy condition, hence the aspect of collaborative positioning. Five low-cost GNSS receivers are used to conduct experiments. According to the result, the positioning accuracy of the receiver in a deep urban area improves from 46.2 to 14.4 m.