mmWave Simultaneous Localization and Mapping Using a Computationally Efficient EK-PHD Filter
Paper i proceeding, 2021

Future cellular networks that utilize millimeter wave signals provide new opportunities in positioning and situational awareness. Large bandwidths combined with large antenna arrays provide unparalleled delay and angle resolution, allowing high accuracy localization but also building up a map of the environment. Even the most basic filter intended for simultaneous localization and mapping exhibits high computational overhead since the methods rely on sigma point or particle-based approximations. In this paper, a first order Taylor series based Gaussian approximation of the filtering distribution is used and it is demonstrated that the developed extended Kalman probability hypothesis density filter is computationally very efficient. In addition, the results imply that efficiency does not come with the expense of estimation accuracy since the method nearly achieves the position error bound.

millimeter wave

extended Kalman filter

probability hypothesis density

Simultaneous localization and mapping

Författare

Ossi Kaltiokallio

Tampereen Yliopisto

Yu Ge

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Jukka Talvitie

Tampereen Yliopisto

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Mikko Valkama

Tampereen Yliopisto

2021 IEEE 24th International Conference on Information Fusion (FUSION)


978-1-7377497-1-4 (ISBN)

2021 IEEE 24th International Conference on Information Fusion (FUSION)
Sun City, South Africa,

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Kommunikationssystem

Sannolikhetsteori och statistik

Signalbehandling

ISBN

9781737749714

Mer information

Senast uppdaterat

2022-02-03