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Deep Learning‐Based Bluetooth Low‐Energy 5.1 Multianchor Indoor Positioning with Attentional Data Filtering.
- Source :
- Advanced Intelligent Systems (2640-4567); Jan2024, Vol. 6 Issue 1, p1-15, 15p
- Publication Year :
- 2024
-
Abstract
- Indoor positioning system (IPS) technologies have widespread applications in logistics, intelligent manufacturing, healthcare monitoring, etc. The recently released Bluetooth low‐energy (BLE) 5.1 specification enables in‐phase and quadrature‐phase (I/Q) data measurements. It allows angle of arrival estimation and becomes a natural choice for IPS implementation. Conventional BLE 5.1 IPSs use multiple anchors to provide massive redundancy to improve system robustness. It however demands effective approaches to leverage redundancy. Besides, interference due to various environmental factors can introduce severe errors to I/Q data and affect positioning accuracy. Facing these challenges, herein, a novel deep learning‐based multianchor BLE 5.1 IPS is proposed. The system aggregates measurements from multiple anchors and makes them available at regular time steps. Then, a novel attentional filtering network tailored to infer high‐quality I/Q sample data is developed and a spatial regularization loss incorporating spatial location relationships to strengthen the feature embedding discrimination is proposed. Two multianchor BLE 5.1 I/Q sample datasets are developed and released for public download. Numerical experiments are carried out to compare the proposed method with previous BLE 5.1 IPS methods and methods utilizing other radio frequency data. Results indicate that the proposed method consistently achieves submeter accuracy and significantly outperforms the state‐of‐the‐art approaches. [ABSTRACT FROM AUTHOR]
- Subjects :
- DEEP learning
INDOOR positioning systems
ARTIFICIAL neural networks
RADIO frequency
Subjects
Details
- Language :
- English
- ISSN :
- 26404567
- Volume :
- 6
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Advanced Intelligent Systems (2640-4567)
- Publication Type :
- Academic Journal
- Accession number :
- 174935968
- Full Text :
- https://doi.org/10.1002/aisy.202300292