5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion
Journal article, 2020
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.
cooperative positioning and mapping
Message passing
Millimeter wave technology
probability hypothesis density
Radio frequency
Wireless communication
map fusion
Simultaneous localization and mapping
vehicular networks
Antenna arrays
5G mobile communication
5G millimeter-wave
Author
Hyowon Kim
Hanyang University
Karl Granström
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Lin Gao
University of Florence
Giorgio Battistelli
University of Florence
Sunwoo Kim
Hanyang University
Henk Wymeersch
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
IEEE Transactions on Wireless Communications
15361276 (ISSN) 15582248 (eISSN)
Vol. 19 6 3782-3795 9032328Multi-dimensional Signal Processing with Frequency Comb Transceivers
Swedish Research Council (VR) (2018-03701), 2018-12-01 -- 2021-12-31.
Subject Categories
Communication Systems
Robotics
Signal Processing
DOI
10.1109/TWC.2020.2978479
Related datasets
5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD Filter and Map Fusion [dataset]
DOI: 10.21227/7f99-yd53