Leveraging Single-Bounce Reflections and Onboard Motion Sensors for Enhanced 5G Positioning

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 line-of-sight (LoS) 5G signals 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 single-bounce reflections (SBRs) and LoS signals to improve positioning performance in dense urban environments. In addition, we integrate the proposed 5G-based positioning with a low-cost inertial measurement unit (IMU) and a wheel encoder. Moreover, the integration is realized using an unscented Kalman filter (UKF) as an alternative to the widely utilized extended Kalman filter (EKF) within the 5G-based positioning research community. We performed two test trajectories in the dense urban environment of downtown Toronto, Canada. For each trajectory, quasi-real 5G measurements were generated using a ray-tracing tool incorporating 3D map scans of real-world buildings, allowing for realistic NLoS and multipath scenarios. For the same trajectories, real motion 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 SBR signals are not considered, which is only around 92% of the time.