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
non-linear 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.Comment: 6 pages, 5 figures, IEEE WCNC 2018 (Wireless Communications and
Networking Conference
