UAV Aided Vehicle Positioning with Imperfect Data Association

In typical autonomous driving, a lane-level (submeter) accuracy and ubiquitous coverage is required. Since the signals from the widely adopted Global Navigation Satellite Systems (GNSS) are frequently attenuated or blocked in urban areas, reliable and high accuracy positioning alternatives are thus required. In this paper, we propose an unmanned aerial vehicle (UAV) aided vehicle positioning framework, combined with the general non-parametric belief propagation (NBP) method, to improve the positioning accuracy of vehicles at blind spots. Aiming at the data association issue during the UAV detection, a cluster-based two-step joint probabilistic data association (JPDA) method is adopted. Furthermore, we find that in the multi-UAV scenarios, proper messages feedback of vehicles can effectively improve the data association, then further enhance the positioning accuracy. Numerical results are provided, to validate our analysis, and show significant performance advantages.