5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion
Artikel i vetenskaplig tidskrift, 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


Hyowon Kim

Hanyang University

Karl Granström

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Lin Gao

Universita degli Studi di Firenze

Giorgio Battistelli

Universita degli Studi di Firenze

Sunwoo Kim

Hanyang University

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

IEEE Transactions on Wireless Communications

15361276 (ISSN) 15582248 (eISSN)

Vol. 19 6 3782-3795 9032328

Multidimensionell signalbehandling med frekvenskammar

Vetenskapsrådet (VR) (2018-03701), 2018-12-01 -- 2021-12-31.



Robotteknik och automation




Relaterade dataset

5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD Filter and Map Fusion [dataset]

DOI: 10.21227/7f99-yd53

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