Abstract
Accurate distance estimation techniques are one of the major requirements for 6G systems. Traditional techniques when using Bluetooth Low Energy (BLE) technology, are based on log-normal shadowing model. However, these models have proven to be incapable of providing the needed accuracy. This paper utilizes a novel method of distance estimation by using a Naïve Bayesian (NB) Classifier to fuse information present in packets from three different advertising channels and classify the distance. This method of distance estimation was tested on a custom dataset and found to have a Mean Absolute Error (MAE) of 0.0234 m. It was compared to the traditional log-normal shadowing curve estimation method and the NB method of fusing channel information was found to outperform it.
Original language | English |
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Title of host publication | Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1 |
Editors | Andrew D. Ball, Huajiang Ouyang, Jyoti K. Sinha, Zuolu Wang |
Publisher | Springer, Cham |
Pages | 591-599 |
Number of pages | 9 |
Volume | 151 |
ISBN (Electronic) | 9783031494130 |
ISBN (Print) | 9783031494123, 9783031494154 |
DOIs | |
Publication status | Published - 30 May 2024 |
Event | The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences - Huddersfield, United Kingdom, Huddersfield, United Kingdom Duration: 29 Aug 2023 → 1 Sep 2023 https://unified2023.org/ |
Publication series
Name | Mechanisms and Machine Science |
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Publisher | Springer |
Volume | 151 MMS |
ISSN (Print) | 2211-0984 |
ISSN (Electronic) | 2211-0992 |
Conference
Conference | The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences |
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Abbreviated title | UNIfied 2023 |
Country/Territory | United Kingdom |
City | Huddersfield |
Period | 29/08/23 → 1/09/23 |
Internet address |