Multiple Model Poisson Multi-Bernoulli Mixture for 5G Mapping
Paper in proceeding, 2020

In this paper, we evaluate and compare the multiple model Poisson multi-Bernoulli mixture (MM-PMBM) and the multiple model probability hypothesis density (MM-PHD) filters for mapping a propagation environment, specified by multiple types objects, using 5G millimeter-wave signals. To develop the MM-PMBM applicable to 5G scenarios, we design the density representation, data structure, and implementation strategy. From the simulation results, it is demonstrated that the MM-PMBM captures the objects and is robust to both missed detections and false alarm compared to the MM-PHD.

Author

Hyowon Kim

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Sunwoo Kim

Hanyang University

2020 Summer Conference of the Korean Institute of Communication Society

2020 Summer Conference of the Korean Institute of Communication Society
, South Korea,

Subject Categories

Other Computer and Information Science

Signal Processing

Computer Vision and Robotics (Autonomous Systems)

Areas of Advance

Information and Communication Technology

More information

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2/3/2022 1