A Design of Multi-Head Attention Neural Network for UWB NLOS Identification in Outdoo 


Vol. 49,  No. 3, pp. 361-364, Mar.  2024
10.7840/kics.2024.49.3.361


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  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

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[IEEE Style]

K. Lee, J. Lee, J. Park, Y. Ko, "A Design of Multi-Head Attention Neural Network for UWB NLOS Identification in Outdoo," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 3, pp. 361-364, 2024. DOI: 10.7840/kics.2024.49.3.361.

[ACM Style]

Kyung-Bo Lee, JiYe Lee, Jongho Park, and Young-Bae Ko. 2024. A Design of Multi-Head Attention Neural Network for UWB NLOS Identification in Outdoo. The Journal of Korean Institute of Communications and Information Sciences, 49, 3, (2024), 361-364. DOI: 10.7840/kics.2024.49.3.361.

[KICS Style]

Kyung-Bo Lee, JiYe Lee, Jongho Park, Young-Bae Ko, "A Design of Multi-Head Attention Neural Network for UWB NLOS Identification in Outdoo," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 3, pp. 361-364, 3. 2024. (https://doi.org/10.7840/kics.2024.49.3.361)
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pISSN : 1226 - 4717
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