April 17, 2024
September 1, 2023
C¸ agkan Yapar, Fabian Jaensch, Ron Levie, Gitta Kutynio, Giuseppe Caire
In dense urban environments, Global Navigation Satellite Systems do not provide good accuracy due to the low probability of line-of-sight (LOS) between the user equipment (UE) to be located and the satellites due to the presence of obstacles such as buildings. As a result, it is necessary to resort to other technologies that can operate reliably under non-line-of-sight (NLOS) conditions. To promote research in the reviving field of radio map-based wireless localization, we have launched the MLSP 2023 Urban Wireless Localization Competition. In this short overview paper, we describe the urban wireless localization problem, the provided datasets and baseline methods, the challenge task, and the challenge evaluation methodology. Finally, we present the results of the challenge.
OVERVIEW OF THE URBAN WIRELESS LOCALIZATION COMPETITION
C¸ agkan Yapar, Fabian Jaensch, Ron Levie, Gitta Kutynio, Giuseppe Caire
2023 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, SEPT. 17–20, 2023, ROME, ITALY
September 1, 2023
C¸ agkan Yapar, Fabian Jaensch, Ron Levie, Gitta Kutynio, Giuseppe Caire
2023 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, SEPT. 17–20, 2023, ROME, ITALY
September 1, 2023