Exploiting Channel Diversity to Improve BLE Range-Finding Accuracy

Samuel Leitch, Qasim Zeeshan Ahmed, Pavlos Lazaridis, Maryam Hafeez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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 languageEnglish
Title of host publicationProceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1
EditorsAndrew D. Ball, Huajiang Ouyang, Jyoti K. Sinha, Zuolu Wang
PublisherSpringer, Cham
Pages591-599
Number of pages9
Volume151
ISBN (Electronic)9783031494130
ISBN (Print)9783031494123, 9783031494154
DOIs
Publication statusPublished - 30 May 2024
EventThe UNIfied Conference of DAMAS, InCoME and TEPEN Conferences - Huddersfield, United Kingdom, Huddersfield, United Kingdom
Duration: 29 Aug 20231 Sep 2023
https://unified2023.org/

Publication series

NameMechanisms and Machine Science
PublisherSpringer
Volume151 MMS
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992

Conference

ConferenceThe UNIfied Conference of DAMAS, InCoME and TEPEN Conferences
Abbreviated titleUNIfied 2023
Country/TerritoryUnited Kingdom
CityHuddersfield
Period29/08/231/09/23
Internet address

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