Joint CKF-PHD Filter and Map Fusion for 5G Multi-cell SLAM
Paper in proceedings, 2020

5G is expected to enable simultaneous vehicle localization and environment mapping (SLAM). Furthermore, vehicular networks will be covered with 5G small cells, wherein the map information is collected at each base station (BS) and then fused so as to promote the overall performance of SLAM. In 5G multi-cell SLAM, there are challenges such as the unknown number of targets, uncertainty regarding the association between the targets and the measurements, unknown types of targets, as well as map management among BSs. To address those challenges, we propose a new method for 5G multi-cell SLAM which comprises a joint cubature Kalman filter and multi-model probability hypothesis density, and a map fusion routine. Simulation results demonstrate that the proposed method solves the aforementioned challenges and also improves vehicle state and map estimates.

5G multi-cell SLAM

joint CKF

map fusion

message passing



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 International Conference on Communications

15503607 (ISSN)

Vol. 2020-June 9149211

2020 IEEE International Conference on Communications, ICC 2020
Dublin, Ireland,

Multidimensional coherent communications with microcombs

Swedish Research Council (VR), 2020-12-01 -- 2026-11-30.

Areas of Advance

Information and Communication Technology

Subject Categories

Communication Systems

Other Medical Biotechnology




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3/8/2021 1