Multiple Model Poisson Multi-Bernoulli Mixture for 5G Mapping
Paper i 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.
Författare
Hyowon Kim
Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk
Henk Wymeersch
Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk
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,
Ämneskategorier
Annan data- och informationsvetenskap
Signalbehandling
Datorseende och robotik (autonoma system)
Styrkeområden
Informations- och kommunikationsteknik