Positioning of high-speed trains using 5G new radio synchronization signals

Abstract

We study positioning of high-speed trains in 5G new radio (NR) networks by utilizing specific NR synchronization signals. The studies are based on simulations with 3GPP-specified radio channel models including path loss, shadowing and fast fading effects. The considered positioning approach exploits measurement of Time-Of-Arrival (TOA) and Angle-Of-Departure (AOD), which are estimated from beamformed NR synchronization signals. Based on the given measurements and the assumed train movement model, the train position is tracked by using an Extended Kalman Filter (EKF), which is able to handle the nonlinear relationship between the TOA and AOD measurements, and the estimated train position parameters. It is shown that in the considered scenario the TOA measurements are able to achieve better accuracy compared to the AOD measurements. However, as shown by the results, the best tracking performance is achieved, when both of the measurements are considered. In this case, a very high, sub-meter, tracking accuracy can be achieved for most (>75%) of the tracking time, thus achieving the positioning accuracy requirements envisioned for the 5G NR. The pursued high-accuracy and high-availability positioning technology is considered to be in a key role in several envisioned HST use cases, such as mission-critical autonomous train systems.

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

“…How to exploit these new characteristics is a largely open problem. As this paper is being written, very few or no publications can be found concerning NR and indoor localization, whereas some initial studies are available for outdoor communications [272].…”

Hybridized-GNSS Approaches to Train Positioning: Challenges and Open Issues on Uncertainty

“…High-speed train positioning using 5G only is simulated in [43], where a track of over 43 km is modeled, with variable train velocity of up to 400 km/h. Using channel models suitable for the considered scenario, and AoD and ToA measurements with the EKF-based tracking model, the authors declare a mean positioning accuracy of 0.66 m (below the 1 m 5G target), with 95% errors below 1.7 m, and 99% ones below 2.3 m. Separate PSS/SSS sequence identities are assigned to RRHs uniformly located at 500 m intervals along the upper side of the railroad, at a distance of 15 m from the railroad.…”

“…Despite the promising outcomes, it is observed that, whenever the train acceleration changes, the tracking algorithm introduces a lag, which affects the tracking accuracy over a certain period. In the works by both Maymo-Camps et al [38] and Talvitie et al [43], the attained performances are expressed in terms of mean positioning accuracy; however, details about the statistical distribution of the measurements are not provided, and the coverage factor used to compute the accuracy is not specified as well. Additionally, despite being based on the fusion of different technologies, i.e., GNSS and 5G, the former study does not analyze the uncertainty propagation associated to the combination of different quantities to derive the position information.…”

“…clear-sky: 0.320 m, in (N20 E20) PPP&5G Ground truth: geo-ref. position in open-sky; urban: 0.918 m, in (N20 E20) 5G only precisely computed position canyon: 0.7272 m, in (N20 E20) 5G only in urban and canyon environments 5G only [43] Simulated track of over 43 km mean positioning accuracy: 0.66 m with variable train velocity of up to 400 km/h 95% errors < 1.7 m, 99% errors < 2.3 m According to the GUM, the measurement procedure shall be replicable and repeatable under the same measurement conditions. In real railway operation conditions, the positioning accuracy attained by the different studies shows strong variations; at the same time, the dynamic train localization accuracy is among one of the most relevant KPIs of the GNSS/hybridized-GNSS receiver, mandated by safety constraints in railway applications.…”

“…The use of mmWave technology is one of 5G communications' key aspects: it enables high data rates and beamforming [42] for AoA positioning, through the use of a large number of elements on antenna arrays for massive multiple-input-multiple-output (MIMO) systems. In [43], high-speed train positioning using specific, beamformed 5G New Radio (NR) downlink synchronization signals (SS) is investigated, based on the sub-meter positioning accuracy requirements specified by the 3GPP Release 14. The Primary Synchronization Signal (PSS), the Secondary Synchronization Signal (SSS), and the Physical Broadcast Channel (PBCH) are transmitted by a network of fixed Remote Radio Heads (RRHs): this ensures very good built-in correlation properties, resulting in accurate ToA and Angle of Departure (AoD) measurements.…”