Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence
Position, Location and Navigation (PLAN) Group, Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive, N.W., Calgary, AB T2N 1N4, Canada
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Sensors 2020, 20(5), 1350; https://doi.org/10.3390/s20051350
Received: 2 February 2020
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Revised: 21 February 2020
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Accepted: 25 February 2020
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Published: 29 February 2020
(This article belongs to the Special Issue Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation)
One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.
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Keywords:
trilateration; BLE; artificial intelligence; localization; obstacle
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MDPI and ACS Style
Naghdi, S.; O’Keefe, K. Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence. Sensors 2020, 20, 1350. https://doi.org/10.3390/s20051350
AMA Style
Naghdi S, O’Keefe K. Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence. Sensors. 2020; 20(5):1350. https://doi.org/10.3390/s20051350
Chicago/Turabian StyleNaghdi, Sharareh, and Kyle O’Keefe. 2020. "Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence" Sensors 20, no. 5: 1350. https://doi.org/10.3390/s20051350
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