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 speed train scenario. The proposed algorithm is tested in two different scenarios. The first is a straight track scenario and the second is a part of a real-life track between Shanghai and Beijing using data from OpenStreetMaps with the map points joined using cubic Bezier curves. The algorithm achieves sub-meter accuracy on the straight track scenario using just one Remote-Radio-Head. For the map trajectory generated using cubic Bezier curves, an accuracy of 1.05~m is achieved with a 99\% availability using only one Remote-Radio-Head, and sub-meter accuracy is achieved when using two Remote-Radio-Heads. The performance requirements set out by 3GPP for the use case of machine control and intelligent transportation are met with just one Remote-Radio-Head.