A Novel Optimized iBeacon Localization Algorithm Modeling

The conventional methods for indoor localization rely on technologies such as RADAR, ultrasonic, laser range localization, beacon technology, and others. Developers in the industry have started utilizing these localization techniques in iBeacon systems that use Bluetooth sensors to measure the objec...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 23; no. 14; p. 6560
Main Authors: Yu, Zhengyu, Chu, Liu, Shi, Jiajia
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 20.07.2023
MDPI
Subjects:
Accuracy
Advertising
Algorithms
Bluetooth low energy
Bluetooth technology
Calibration
Communication
Energy consumption
iBeacon localization
Kalman filter
Localization
Methods
received signal strength indicator
Receivers & amplifiers
Sensors
Smartphones
wireless sensor networks
iBeacon localization
Bluetooth low energy
wireless sensor networks
Kalman filter
received signal strength indicator
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:The conventional methods for indoor localization rely on technologies such as RADAR, ultrasonic, laser range localization, beacon technology, and others. Developers in the industry have started utilizing these localization techniques in iBeacon systems that use Bluetooth sensors to measure the object’s location. The iBeacon-based system is appealing due to its low cost, ease of setup, signaling, and maintenance; however, with current technology, it is challenging to achieve high accuracy in indoor object localization or tracking. Furthermore, iBeacons’ accuracy is unsatisfactory, and they are vulnerable to other radio signal interference and environmental noise. In order to address those challenges, our study focuses on the development of error modeling algorithms for signal calibration, uncertainty reduction, and interfered noise elimination. The new error modeling is developed on the Curve Fitted Kalman Filter (CFKF) algorithms. The reliability, accuracy, and feasibility of the CFKF algorithms are tested in the experiments. The results significantly show the improvement of the accuracy and precision with this novel approach for iBeacon localization.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23146560