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

Positioning of High-speed Trains using 5G New Radio Synchronization Signals