Localization and Tracking of High-speed Trains Using Compressed Sensing Based 5G Localization Algorithms

Abstract

Complex systems are in place for the localization and tracking of High-speed Trains. These methods tend to perform poorly under certain conditions. Localization using 5G infrastructure has been considered as an alternative solution for the positioning of trains in previous studies. However, these studies only consider localization using Time Difference of Arrival measurements or using Time of Arrival and Angle of Departure measurements. In this paper an alternate compressed sensing based 5G localization method is considered for this problem. The proposed algorithm, paired with an Extended Kalman Filter, is implemented and tested on a 3GPP specified high s peed train scenario. Sub-meter localization accuracy was achieved using 4-6 Remote-Radio-Heads, while an accuracy of 0.34 m with 95% availability is achieved when using 2 Remote-Radio-Heads. The achieved performance meets 3GPP specified requirement for machine control and transportation even when using 2 Remote-Radio-Heads.