Research on a reference signal optimisation algorithm for indoor Bluetooth positioning
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,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2021-11-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2021.2.00102 |
| _version_ | 1797258364168699904 |
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| author | Luo Heng Hu Xinyu Zou Youmin Jing Xinglei Song Chengyi Ni Qidong |
| author_facet | Luo Heng Hu Xinyu Zou Youmin Jing Xinglei Song Chengyi Ni Qidong |
| author_sort | Luo Heng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T04:41:09Z |
| format | Article |
| id | doaj.art-2f928b1518304d659eab232dcc6d9609 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-04-24T22:52:21Z |
| publishDate | 2021-11-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-2f928b1518304d659eab232dcc6d96092024-03-18T10:29:01ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562021-11-017267568410.2478/amns.2021.2.00102Research on a reference signal optimisation algorithm for indoor Bluetooth positioningLuo Heng0Hu Xinyu1Zou Youmin2Jing Xinglei3Song Chengyi4Ni Qidong5Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, 1#, Ke Rui Road, Suzhou New District, Suzhou, ChinaSuzhou University of Science and Technology, 1#, Ke Rui Road, Suzhou New District, Suzhou, ChinaSuzhou University of Science and Technology, 1#, Ke Rui Road, Suzhou New District, Suzhou, ChinaSuzhou University of Science and Technology, 1#, Ke Rui Road, Suzhou New District, Suzhou, ChinaState Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd, Shanghai200240, PR ChinaJiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, 1#, Ke Rui Road, Suzhou New District, Suzhou, ChinaGPS 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.https://doi.org/10.2478/amns.2021.2.00102bluetoothdeep learninggaussian filterindoor positioningsupervised learning |
| spellingShingle | Luo Heng Hu Xinyu Zou Youmin Jing Xinglei Song Chengyi Ni Qidong Research on a reference signal optimisation algorithm for indoor Bluetooth positioning Applied Mathematics and Nonlinear Sciences bluetooth deep learning gaussian filter indoor positioning supervised learning |
| title | Research on a reference signal optimisation algorithm for indoor Bluetooth positioning |
| title_full | Research on a reference signal optimisation algorithm for indoor Bluetooth positioning |
| title_fullStr | Research on a reference signal optimisation algorithm for indoor Bluetooth positioning |
| title_full_unstemmed | Research on a reference signal optimisation algorithm for indoor Bluetooth positioning |
| title_short | Research on a reference signal optimisation algorithm for indoor Bluetooth positioning |
| title_sort | research on a reference signal optimisation algorithm for indoor bluetooth positioning |
| topic | bluetooth deep learning gaussian filter indoor positioning supervised learning |
| url | https://doi.org/10.2478/amns.2021.2.00102 |
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