  5G-based mmWave wireless positioning has emerged as a promising solution for
autonomous vehicle (AV) positioning in recent years. Previous studies have
highlighted the benefits of fusing a line-of-sight (LoS) 5G positioning
solution with an Inertial Navigation System (INS) for an improved positioning
solution. However, the highly dynamic environment of urban areas, where AVs are
expected to operate, poses a challenge, as non-line-of-sight (NLoS)
communication can deteriorate the 5G mmWave positioning solution and lead to
erroneous corrections to the INS. To address this challenge, we exploit 5G
multipath and LoS signals to improve positioning performance in dense urban
environments. In addition, we integrate the proposed 5G-based positioning with
low-cost onboard motion sensors (OBMS). Moreover, the integration is realized
using an unscented Kalman filter (UKF) as an alternative to the widely utilized
EKF as a fusion engine to avoid ignoring the higher-order and non-linear terms
of the dynamic system model. We also introduce techniques to evaluate the
quality of each LoS and multipath measurement prior to incorporation into the
filter's correction stage. To validate the proposed methodologies, we performed
two test trajectories in the dense urban environment of downtown Toronto,
Canada. For each trajectory, quasi-real 5G measurements were collected using a
ray-tracing tool incorporating 3D map scans of real-world buildings, allowing
for realistic multipath scenarios. For the same trajectories, real OBMS data
were collected from two-different low-cost IMUs. Our integrated positioning
solution was capable of maintaining a level of accuracy below 30 cm for
approximately 97% of the time, which is superior to the accuracy level achieved
when multipath signals are not considered, which is only around 91% of the
time.
