Leveraging Single-Bounce Reflections and Onboard Motion Sensors for Enhanced 5G Positioning
Artikel i vetenskaplig tidskrift, 2024

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.

Kalman filtering

multipath

mmWave

5G

sensor fusion

positioning

inertial sensors

Författare

Qamar Bader

Royal Military College of Canada

Queen's University

Sharief Essam Saleh

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Mohamed Elhabiby

Micro Engineering Tech. Inc.

Ain Shams University

Aboelmagd Noureldin

Queen's University

Royal Military College of Canada

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN) 1558-0016 (eISSN)

Vol. In Press

Ämneskategorier

Kommunikationssystem

Robotteknik och automation

Signalbehandling

DOI

10.1109/TITS.2024.3480525

Mer information

Senast uppdaterat

2024-12-02