A Practice of BLE RSSI Measurement for Indoor Positioning.

BLE IPS Kalman filter RSSI modification coefficient trilateration

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
30 Jul 2021
Historique:
received: 22 06 2021
revised: 20 07 2021
accepted: 27 07 2021
entrez: 10 8 2021
pubmed: 11 8 2021
medline: 11 8 2021
Statut: epublish

Résumé

Bluetooth Low Energy (BLE) is one of the RF-based technologies that has been utilizing Received Signal Strength Indicators (RSSI) in indoor position location systems (IPS) for decades. Its recent signal stability and propagation distance improvement inspired us to conduct this project. Beacons and scanners used two Bluetooth specifications, BLE 5.0 and 4.2, for experimentations. The measurement paradigm consisted of three segments, RSSI-distance conversion, multi-beacon in-plane, and diverse directional measurement. The analysis methods applied to process the data for precise positioning included the Signal propagation model, Trilateration, Modification coefficient, and Kalman filter. As the experiment results showed, the positioning accuracy could reach 10 cm when the beacons and scanners were at the same horizontal plane in a less-noisy environment. Nevertheless, the positioning accuracy dropped to a meter-scale accuracy when the measurements were executed in a three-dimensional configuration and complex environment. According to the analysis results, the BLE wireless signal strength is susceptible to interference in the manufacturing environment but still workable on certain occasions. In addition, the Bluetooth 5.0 specifications seem more promising in bringing brightness to RTLS applications in the future, due to its higher signal stability and better performance in lower interference environments.

Identifiants

pubmed: 34372415
pii: s21155181
doi: 10.3390/s21155181
pmc: PMC8347277
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Ministry of Science and Technology, Taiwan
ID : 109-2222-E-011-008

Références

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IEEE Trans Neural Netw. 1993;4(4):570-90
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Auteurs

Ramiro Ramirez (R)

Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 106344, Taiwan.

Chien-Yi Huang (CY)

Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 106344, Taiwan.

Che-An Liao (CA)

Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.

Po-Ting Lin (PT)

Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.
Industry 4.0 Implementation Center, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.

Hsin-Wei Lin (HW)

Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.

Shu-Hao Liang (SH)

Industry 4.0 Implementation Center, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.

Classifications MeSH