@article{M10CB0F63,
title = "A Design of Multi-Head Attention Neural Network for UWB NLOS Identification in Outdoo",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2024",
issn = "1226-4717",
doi = "10.7840/kics.2024.49.3.361",
author = "Kyung-Bo Lee, JiYe Lee, Jongho Park, Young-Bae Ko",
keywords = "UWB, Multi-head attention, CIR, LOS/NLOS",
abstract = "In this paper, we introduce a method of classifying UWB CIR data into LOS and NLOS environments by applying the multi-head attention algorithm. The 1016 UWB CIR values sampled at 100 ms intervals are divided into 100 segments. By comparing the classification time and accuracy of the LSTM-CNN algorithm and the multi-head attention algorithm, it is shown that the latter achieved a classification accuracy of 94.41% for LOS/NLOS environments, outperforming the LSTM-CNN model"
}