Abstract:
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...Show MoreMetadata
Abstract:
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.
Published in: 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)
Date of Conference: 17-20 September 2023
Date Added to IEEE Xplore: 23 October 2023
ISBN Information: