Enhanced Land Vehicle Positioning in Challenging GNSS Urban Environments Utilizing Automotive Radars

Positioning services for land vehicle navigation have long relied on global navigation satellite systems (GNSS); however, GNSS cannot maintain an accurate position while travelling under bridges, around tall buildings and under tree canopies due to signal blockage or multipath. Integration with the onboard motion sensors can bridge the positioning solution for a short duration but cannot sustain adequate positioning performance during extended GNSS outages. The proposed research overcomes the limitations of current positioning technologies for land vehicles by integrating a GNSS receiver, onboard motion sensors, and an electronic scanning radar (ESR) used in present land vehicles for adaptive cruise control. A new ESR-based static object detection method is developed using newly designed criteria to estimate the forward velocity. We also propose a radar odometry method to obtain the vehicle position based on the radar scans. Integration with onboard motion sensors based on Extended Kalman filtering (EKF) is designed and realized to achieve accurate positioning in degraded vision and challenging GNSS environments. The proposed multi-sensor positioning solution is examined using several road test trajectories in challenging urban environments in Toronto. It shows RMS error as low as 3 m over more than 90% of the traveled distance.