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.

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.