Deep Learning-Based Indoor Localization Using Multi-View BLE Signal
In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) va...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/7/2759 |
| _version_ | 1797437661429891072 |
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| author | Aristotelis Koutris Theodoros Siozos Yannis Kopsinis Aggelos Pikrakis Timon Merk Matthias Mahlig Stylianos Papaharalabos Peter Karlsson |
| author_facet | Aristotelis Koutris Theodoros Siozos Yannis Kopsinis Aggelos Pikrakis Timon Merk Matthias Mahlig Stylianos Papaharalabos Peter Karlsson |
| author_sort | Aristotelis Koutris |
| collection | DOAJ |
| description | In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA estimation using varying subsets of anchor points. In the final stage of the system, the estimated AoA values are fed to a positioning engine which uses the least squares (LS) algorithm to estimate the position of the tag. The proposed architectures are trained and rigorously tested on several simulated room scenarios and are shown to achieve a localization accuracy of 70 cm. Moreover, the proposed systems possess generalization capabilities by being robust to modifications in the room’s content or anchors’ configuration. Additionally, some of the proposed architectures have the ability to distribute the computational load over the APs. |
| first_indexed | 2024-03-09T11:25:39Z |
| format | Article |
| id | doaj.art-aea1cff883354d2f9842d221e051c2c3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:25:39Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-aea1cff883354d2f9842d221e051c2c32023-12-01T00:05:04ZengMDPI AGSensors1424-82202022-04-01227275910.3390/s22072759Deep Learning-Based Indoor Localization Using Multi-View BLE SignalAristotelis Koutris0Theodoros Siozos1Yannis Kopsinis2Aggelos Pikrakis3Timon Merk4Matthias Mahlig5Stylianos Papaharalabos6Peter Karlsson7Libra AI Technologies, 11854 Athens, GreeceLibra AI Technologies, 11854 Athens, GreeceLibra AI Technologies, 11854 Athens, GreeceLibra AI Technologies, 11854 Athens, GreeceU-Blox AG, 8800 Thalwil, SwitzerlandU-Blox AG, 8800 Thalwil, SwitzerlandU-Blox AG, 8800 Thalwil, SwitzerlandU-Blox AG, 8800 Thalwil, SwitzerlandIn this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA estimation using varying subsets of anchor points. In the final stage of the system, the estimated AoA values are fed to a positioning engine which uses the least squares (LS) algorithm to estimate the position of the tag. The proposed architectures are trained and rigorously tested on several simulated room scenarios and are shown to achieve a localization accuracy of 70 cm. Moreover, the proposed systems possess generalization capabilities by being robust to modifications in the room’s content or anchors’ configuration. Additionally, some of the proposed architectures have the ability to distribute the computational load over the APs.https://www.mdpi.com/1424-8220/22/7/2759indoor localizationBLEdeep neural networksangle of arrival |
| spellingShingle | Aristotelis Koutris Theodoros Siozos Yannis Kopsinis Aggelos Pikrakis Timon Merk Matthias Mahlig Stylianos Papaharalabos Peter Karlsson Deep Learning-Based Indoor Localization Using Multi-View BLE Signal Sensors indoor localization BLE deep neural networks angle of arrival |
| title | Deep Learning-Based Indoor Localization Using Multi-View BLE Signal |
| title_full | Deep Learning-Based Indoor Localization Using Multi-View BLE Signal |
| title_fullStr | Deep Learning-Based Indoor Localization Using Multi-View BLE Signal |
| title_full_unstemmed | Deep Learning-Based Indoor Localization Using Multi-View BLE Signal |
| title_short | Deep Learning-Based Indoor Localization Using Multi-View BLE Signal |
| title_sort | deep learning based indoor localization using multi view ble signal |
| topic | indoor localization BLE deep neural networks angle of arrival |
| url | https://www.mdpi.com/1424-8220/22/7/2759 |
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