NLoS Detection for Enhanced 5G mmWave-based Positioning for Vehicular IoT Applications

5G NR mm Wave promises accurate positioning down to the centimeter level. However, mmWave signals endure prismatic propagation, making them prone to signal blockages and non-line of sight (NLoS) communications. To achieve a precise positioning solution, it is rather essential to filter out NLoS gNBs as they yield erroneous pose estimation of IoT vehicular applications. Previous works have attempted to address this issue, however, they are either based on impractical or invalid assumptions about the operation scenario. In this paper, a novel, yet, simple and realistic NLoS detection algorithm is developed. The proposed method measures the discrepancy between Received Signal Strength (RSS)-based and time-based ranges as means to detect NLoS operation. To validate the proposed NLoS detection method, it was incorporated into a Kalman Filter (KF) that fuses the angle of departure (AoD) and round trip time (RTT) measurements from multiple gNBs in a loosely coupled fashion. The proposed method was evaluated on quasi-real measurements acquired from a highly validated 5G simulation tool that simulates the cores of downtown Toronto. The proposed method demonstrates superior results, as it sustains a sub-1m level of accuracy for around 95% of the time, as compared to merely 29% of the time without the NLoS detection.