A Novel GNSS Decentralized Cooperative Positioning Algorithm for Internet of Vehicles

With cooperative positioning (CP) in Internet of Vehicles (IoV), the positioning performance can be improved by utilizing the positioning information provided by neighboring vehicles. However, in urban canyons, the CP performance based on global navigation satellite system (GNSS) is severely degraded by multipath and non-line-of-sight (NLOS) effects, and a robust CP algorithm is required. This article proposes a GNSS decentralized CP (DCP) algorithm based on the generalized extreme studentized deviate test (GESD) filter. Further, a new two-stage CP framework is established, consisting of two modules, the independent and connected modules. The independent module is the first filter layer detection for GNSS pseudorange residual error, operating independently within each vehicle to mitigate the impact of abnormal measurements, such as multipath bias and satellite faults. When receiving data from neighboring vehicles, the connected module is activated to detect shared GNSS pseudorange errors and relative pseudorange measurements, further reducing the impact of anomalous measurements. The outdoor experiments validate the superiority of the DCP algorithm over the single-point positioning (SPP) method based on receiver autonomous integrity monitoring fault detection and exclusion (RAIM-FDE) in both GPS-only and GPS/BDS combination strategies, especially in blocked situations. The positioning root-mean-square error (RMSE) of the DCP algorithm for horizontal positioning is about 1.00 m in static scenes and about 8.00 m in dynamic scenes. Compared with the SPP-RAIM, the DCP algorithm can improve about 25.22%–43.04% and 16.55%–40.17% on average under GPS/BDS combination strategies in the horizontal and 3-D directions, respectively.