@inproceedings{52a001f16c5949538af67cd562cf867f,
title = "Poster: Identifying Multipath in Phase-Based Ranging Measurements Using Channel Sounding",
abstract = "Multipath is a common phenomenon that influences the accuracy of phase-based measurements. Distance estimation and positioning features are being increasingly integrated in many wireless standards to meet the growing need for indoor localization mechanisms. Previously used methods, such as RSSI ranging are not accurate enough, especially in multipath en-vironments. The Bluetooth SIG is currently working towards enabling high-Accuracy distance estimation between Bluetooth devices [1]. Although this approach is more advanced it is still highly affected by multi path fading which impacts accuracy. In this work, a method to detect multipath based on IQ data samples and machine learning is presented. The aim is to improve accuracy, especially for measurements inside a vehicle, for phase-based distance algorithms, or for labeling IQ data samples for supervised and semi-supervised machine learning tasks. The data used for these evaluations takes into account three different environments which have different multipath characteristics.",
keywords = "Bluetooth Channel Sounding, Indoor, Localization, Multipath, Vehicle",
author = "Elin Eriksson and Walter Bronzi and Leah Strand and Alois Knoll",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 15th IEEE Vehicular Networking Conference, VNC 2024 ; Conference date: 29-05-2024 Through 31-05-2024",
year = "2024",
doi = "10.1109/VNC61989.2024.10575979",
language = "English",
series = "IEEE Vehicular Networking Conference, VNC",
publisher = "IEEE Computer Society",
pages = "271--272",
editor = "Susumu Ishihara and Hiroshi Shigeno and Onur Altintas and Takeo Fujii and Raphael Frank and Florian Klingler and Tobias Hardes and Tobias Hardes",
booktitle = "2024 IEEE Vehicular Networking Conference, VNC 2024",
}