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,
, South Korea,
Subject Categories
Other Computer and Information Science
Signal Processing
Computer Vision and Robotics (Autonomous Systems)
Areas of Advance
Information and Communication Technology