Research on a reference signal optimisation algorithm for indoor Bluetooth positioning

dc.contributor.authorLuo, Heng
dc.contributor.authorHu, Xinyu
dc.contributor.authorZou, Youmin
dc.contributor.authorJing, Xinglei
dc.contributor.authorSong, Chengyi
dc.contributor.authorNi, Qidong
dc.date.accessioned2022-01-19T12:37:33Z
dc.date.available2022-01-19T12:37:33Z
dc.date.copyright© 2021
dc.date.issued2021
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this article paper is published in Applied Mathematics and Nonlinear Sciences (2021), available online at: https://doi.org/10.2478/amns.2021.2.00111en_US
dc.description.abstractGPS has a sharp performance decline in terms of accuracy indoors due to the complex building structure. A combined algorithm, targeting at received signal strength indication (RSSI) calibration optimisation, depending on deep neural network training via input vector Γ and the target output vector ψ, termed reference signal optimisation algorithm (RSOA) is proposed to improve the positioning accuracy in the indoor Bluetooth positioning networks. Experimental results show that the relative error of the proposed RSOA between the estimated results and the measured ones can reach as low as 0.2%, and the absolute errors can be reduced to 0.13 m at most within 10 m. © 2021 Heng Luo et al., published by Sciendo 2021.en_US
dc.description.sponsorshipNational Science Foundation - 51874205, 51973109, 61602334 This paper is funded by the NSF (61602334, 51874205, 51973109).en_US
dc.identifier.citationLuo, H., Hu, X., Zou, Y., Jing, X., Song, C. & Ni, Q. (2021). Research on a reference signal optimisation algorithm for indoor Bluetooth positioning. Applied Mathematics and Nonlinear Sciences,6(2) 525-534. https://doi.org/10.2478/amns.2021.2.00111en_US
dc.identifier.issn24448656
dc.identifier.urihttps://doi.org/10.2478/amns.2021.2.00111
dc.identifier.urihttp://hdl.handle.net/20.500.12519/500
dc.language.isoenen_US
dc.publisherSciendoen_US
dc.relationAuthors Affiliations : Luo, H., Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, 1#, Ke Rui Road, Suzhou New District, Suzhou, China; Hu, X., Suzhou University of Science and Technology, 1#, Ke Rui Road, Suzhou New District, Suzhou, China; Zou, Y., Suzhou University of Science and Technology, 1#, Ke Rui Road, Suzhou New District, Suzhou, China; Jing, X., Suzhou University of Science and Technology, 1#, Ke Rui Road, Suzhou New District, Suzhou, China; Song, C., State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd, Shanghai, 200240, China; Ni, Q., Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, 1#, Ke Rui Road, Suzhou New District, Suzhou, China, State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd, Shanghai, 200240, China, Abaido, Media and Communication Studies, Faculty of Communication, Arts and Sciences (FCAS), Canadian University-Dubai, Dubai, United Arab Emirates, Alaghbari, Business Administration, Administrative Sciences, Applied Science University, Al Eker, Bahrain
dc.relation.ispartofseriesApplied Mathematics and Nonlinear Sciences;
dc.rightsCreative Commons Attribution 4.0 International License.
dc.rights.holderCopyright : © 2021 Heng Luo et al., published by Sciendo 2021.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBluetoothen_US
dc.subjectdeep learningen_US
dc.subjectGaussian filteren_US
dc.subjectindoor positioningen_US
dc.subjectsupervised learningen_US
dc.titleResearch on a reference signal optimisation algorithm for indoor Bluetooth positioningen_US
dc.typeArticleen_US
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