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.

Wireless communication

map fusion

cooperative positioning and mapping

vehicular networks

Simultaneous localization and mapping

Radio frequency

5G millimeter-wave

5G mobile communication

probability hypothesis density

Antenna arrays

Message passing

Millimeter wave technology

Author

Hyowon Kim

Hanyang University

Karl Granström

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Lin Gao

University of Florence

Giorgio Battistelli

University of Florence

Sunwoo Kim

Hanyang University

Henk Wymeersch

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

IEEE Transactions on Wireless Communications

1536-1276 (ISSN)

Vol. 19 6 3782-3795

Multi-dimensional Signal Processing with Frequency Comb Transceivers

Swedish Research Council (VR), 2018-12-01 -- 2021-12-31.

Subject Categories

Communication Systems

Robotics

Signal Processing

DOI

10.1109/TWC.2020.2978479

More information

Latest update

9/18/2020