5G mmWave Integrated Positioning for Autonomous Vehicles in Urban Environments

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Saleh, Sharief
5G , mmWave , Positioning and Localization , Autonomous Vehicles , Sensor Fusion , Kalman Filtering , Onboard Motion Sensors , Inertial Navigation Systems
Autonomous vehicles (AVs) have the potential to revolutionize the transportation industry. Yet, achieving higher levels of autonomy requires a positioning solution that is accurate, reliable, and independent of the environment. This thesis addresses this challenge by focusing on high-precision positioning for AVs in urban environments using the emerging 5G mmWave technology and integrating it with onboard motion sensors (OBMS). The goal of this research is to design a seamless positioning solution that can achieve an uninterrupted decimeter level of accuracy for 95% of the time in all environments, weather conditions, and operating dynamics. To achieve this goal, three novel methodologies are proposed. The first methodology proposes a dynamic tuning approach for the extended Kalman filter's (EKF) covariance matrix while operating in a standalone 5G trilateration mode, where only range measurements are available. This approach utilizes range measurements from at least three base stations (BSs) and adjusts the reliability of these measurements based on their proximity to the user equipment (UE). This method significantly improves the accuracy compared to traditional EKF trilateration implementations, as it mitigates linearization errors arising from nearby BSs. In case angle measurements were available, a decentralized standalone 5G multi-BS hybrid solution is suggested, which requires a single BS to operate. The proposed decentralized wireless positioning solution completely eliminates the unnecessary linearization errors encountered by its centralized counterparts, which are traditionally used in recently published research. The proposed solution is able to achieve high-precision positioning in dense urban environments with a positioning error of less than 30 cm for 94% of the time. In order to overcome the possibility of 5G signal blockage in dense urban environments, this research develops a loosely coupled (LC) multi-system sensor fusion to integrate 5G and OBMS positioning systems. Unlike tightly coupled (TC) approaches proposed in the literature, LC integration will not incur linearization errors due to the linear measurement model utilized. The proposed methodology achieves a 14-cm level of accuracy for more than 95% of the time while significantly bounding the positioning errors during 5G outages.
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