Hybrid GNSS +5G Position and Rotation Estimation Algorithm Based on TOA and Unit Vector of Arrival in Urban Environment

Positioning methods that rely solely on global satellite navigation system (GNSS) measurements have significantly decreased reliability in urban environments and cannot meet the needs of industries, such as autonomous driving. This article introduces a novel fifth-generation (5G) joint time of arrival (TOA) and unit vector observation model that takes rotation into account, and evaluates its performance through multiple sets of simulated data. The results show that compared with the traditional joint TOA and angle of arrival (AOA) observation model, the proposed model can notably improve the positioning accuracy, and the effect is better after iteration. Furthermore, we conduct a new stochastic model for 5G measurements and investigate the impact of model coefficients on performance. The results reveal that inappropriate coefficients will lead to loss of observational information, highlighting the importance of stochastic models. In urban environments, the proposed 5G joint observation model can effectively assist GNSS and provide more accurate navigation services and rotation estimation, providing valuable insights for the future development of smart transportation.