5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. We propose a new method for cooperative vehicle positioning and mapping of the radio environment, comprising a multiple-model probability hypothesis density filter and a map fusion routine, which is able to consider different types of objects and different fields of views. Simulation results demonstrate the performance of the proposed method.
@article{arxiv.1908.09806,
title = {5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD Filter and Map Fusion},
author = {Hyowon Kim and Karl Granström and Lin Gao and Giorgio Battistelli and Sunwoo Kim and Henk Wymeersch},
journal= {arXiv preprint arXiv:1908.09806},
year = {2020}
}
Comments
This work has been accepted in the IEEE Transactions on Wireless Communications