5th generation (5G) millimeter wave (mmWave) positioning systems are of growing interest for application in operating environments where global navigation satellite system (GNSS) signals are unavailable or unreliable, promising enhancements to positioning accuracy. Application environments range from warehouses and indoor areas to dense urban spaces. However, in real dynamic operating conditions, brief signal outages are expected due to both environmental features and moving objects such as cars or pedestrians. During 5G signal outages, a positioning system must rely on alternative positioning systems and sensors. Inertial navigation systems (INS) provide a self-contained positioning solution unaffected by environmental factors. However, when operating alone INS suffers from unbounded drift in position error. Automotive radar, or electronic scanning radar (ESR) are low-cost sensors integrated in most modern vehicles, and are of increasing interest to positioning applications. This paper presents an extended Kalman filter (EKF) fusion architecture integrating a 5G positioning system with pose corrections from an ESR scan to map registration algorithm. 5G measurements are simulated in a quasi-real environment, and all radar and INS data are collected from real road tests within a GNSS-denied indoor parking garage. An array of 5G signal outages of varying lengths and characteristics are inflicted on the positioning system. The radar-aided positioning system maintains an average root mean squared error of 0.6m during 5G signal outages, improving the 5G/INS performance by 70% across the tested scenarios.

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.

High-precision positioning in areas where Global Navigation Satellite Systems (GNSS) are degraded or unavailable is a necessity for the autonomous vehicles (AVs) of today and the near future and remains an active research problem. Fifthgeneration (5G) millimeter-wave (mmWave) technology presents a promising answer to wireless-based positioning in GNSS-denied environments. Like GNSS however, 5G positioning systems are expected to encounter brief signal outages in real, dynamic driving environments. During these outages, the positioning system must maintain its accuracy until a signal is available once more by relying on alternate technologies. On-board motion sensors (OBMS) including inertial measurement units (IMU)s and odometers are a logical solution to this problem, maintaining a position estimate through dead-reckoning methods. A classic solution is the integration of an odometer, or wheel encoder, with measurements from an IMU. Wheel encoders are limited by a fixed resolution and a relatively low data rate. Electronic Scanning Radar (ESR) are low-cost sensors found on most modern vehicles and measure the range, angle, and Doppler velocity of targets in their environment. In this paper, we explore the use of an ESR for forward velocity estimation as an alternative to the wheel encoder. ESR-based velocity estimation is integrated with 5G positioning, and its ability to maintain high positioning accuracy during 5G signal outages is assessed. Overall, due to an increased resolution and data rate, ESR velocity estimates were found to sustain a higher positioning accuracy during signal outages when compared to wheel-based odometry.

Constrained environments, such as indoor and urban settings, present a significant challenge for accurate moving object positioning due to the diminished line-of-sight (LoS) communication with the wireless anchor used for positioning. The 5th generation new radio (5G NR) millimeter wave (mmWave) spectrum promises high multipath resolvability in the time and angle domains, enabling the utilization of multipath signals for such problems rather than mitigating their effects. This paper investigates the benefits of integrating multipath signals into 5G LoS-based positioning systems with onboard motion sensors (OBMS). We provide a comprehensive analysis of the positioning system’s performance in various conditions of erroneous 5G measurements and outage scenarios, which offers insights into the system’s behavior in challenging environments. To validate our approach, we conducted a road test in downtown Toronto, utilizing actual OBMS measurements gathered from sensors installed in the test vehicle. The results indicate that utilization of multipath signals for wireless positioning operating in multipath-rich environments (e.g. urban and indoor) can bridge 5G LoS signal outages, thus enhancing the reliability and accuracy of the positioning solution. The redundant measurements obtained from the multipath signals can enhance the system’s robustness, particularly when low-cost 5G receivers with a limited angle or range measurements are present. This holds true even when only considering the utilization of single-bounce reflections (SBRs).