5G-positioning for traffic safety and intelligent intersections

Abstract

Fifth generation (5G) of mobile communications enables precise location estimates due to large time-bandwidth products of 5G waveforms and large aperture antenna arrays. In this contribution we discuss and evaluate two hyperbolic approaches to 5G positioning for vehicular applications, namely Friedlander’s method and Chan’s. These may improve the reliability and robustness for position estimates in rural road scenarios compared to estimates from automotive sensor data only. Here, we report on the impact of multipath propagation and cellular network deployment geometry on estimation accuracy and the underlying optimization algorithm has been investigated. Furthermore the results indicate the system requirements for an estimation accuracy for vehicular applications.