<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>GPS 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.</p>
</abstract>

GPS 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.

Research on a reference signal optimisation algorithm for indoor Bluetooth positioning

Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency

Suzhou University of Science and Technology

State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering

Abaido, Media and Communication Studies, Faculty of Communication, Arts and Sciences (FCAS)

Alaghbari, Business Administration, Administrative Sciences

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

GPS has a sharp performance decline in terms of accuracy indoors due to the complex building structure. A combined algorithm, targeting at received signal...

Parameters	Settings

Transmission power of Bluetooth device (mW)	1
Sampling rate (/s)	1
Interval of sampling distance (m)	0.1
Maximum sampling distance (m)	10
The number of samples	19600
The height of the nodes (m)	0.7

Research on a reference signal optimisation algorithm for indoor Bluetooth positioning

Publicado en línea: 29 nov 2021

Páginas: 675 - 684

Recibido: 14 dic 2020

Aceptado: 07 jul 2021

DOI: https://doi.org/10.2478/amns.2021.2.00102

Palabras clave
Bluetooth, deep learning, Gaussian filter, indoor positioning, supervised learning

© 2021 Heng Luo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

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Experiment settings

Research on a reference signal optimisation algorithm for indoor Bluetooth positioning

Publicado en línea: 29 nov 2021

Páginas: 675 - 684

Recibido: 14 dic 2020

Aceptado: 07 jul 2021

DOI: https://doi.org/10.2478/amns.2021.2.00102

Palabras claveBluetooth, deep learning, Gaussian filter, indoor positioning, supervised learning

© 2021 Heng Luo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Experiment settings

Palabras clave
Bluetooth, deep learning, Gaussian filter, indoor positioning, supervised learning