5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion
- Authors
- Kim, Hyowon; Granstrom, Karl; Gao, Lin; Battistelli, Giorgio; Kim, Sunwoo; Wymeersch, Henk
- Issue Date
- Jun-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Simultaneous localization and mapping; 5G mobile communication; Radio frequency; Antenna arrays; Message passing; Wireless communication; Millimeter wave technology; 5G millimeter-wave; cooperative positioning and mapping; map fusion; probability hypothesis density; vehicular networks
- Citation
- IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, v.19, no.6, pp.3782 - 3795
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
- Volume
- 19
- Number
- 6
- Start Page
- 3782
- End Page
- 3795
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/9756
- DOI
- 10.1109/TWC.2020.2978479
- ISSN
- 1536-1276
- Abstract
- 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.
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