On a Novel High Accuracy Positioning With Intelligent Reflecting Surface and Unscented Kalman Filter for Intelligent Transportation Systems in B5G
Yishi Zhu, Bomin Mao, Nei Kato
Abstract
High accuracy and simultaneous positioning is an essential demand in future Intelligent Transportation Systems (ITS), while the mobility and dynamics of vehicles place great challenges. Single Base Station (BS) positioning has become popular for its fast speed, high convenience, and low cost. With the construction of 5G, the wide bandwidth and high separation capability of millimeter Wave (mmWave) bring more possibilities for vehicle positioning via single BS. However, mmWave signals have high distance attenuation and are easily blocked by obstacles. In urban scenarios, the prevalent None-Line-of-Sight (NLoS) situations have severe impacts on positioning accuracy. The multipath effects, Doppler effects, and tracking lags further degrade the performance. To address these issues, we introduce the Intelligent Reflecting Surface (IRS) to single BS vehicle positioning for beyond Line-of-Sight (LoS) communications. We study the advantages of IRS in urban ITS to alleviate the multipath effects, Doppler effects, and tracking delay. To realize the real-time target tracking for IRS, the Unscented Kalman Filter (UKF) is adopted, for which stable communications between the BS and moving vehicle can be maintained. Simulation results show that the utilization of IRS can significantly improve the positioning accuracy and the adoption of UKF further enhances the performance.