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http://dx.doi.org/10.6109/jkiice.2021.25.1.75

BLE-based Indoor Positioning System design using Neural Network  

Shin, Kwang-Seong (Department of Digital Contents Engineering, Wonkwang University)
Lee, Heekwon (Department of Information & Communication Engineering Department, Wonkwang University)
Youm, Sungkwan (Department of Information & Communication Engineering Department, Wonkwang University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.1, 2021 , pp. 75-80 More about this Journal
Abstract
Positioning technology is performing important functions in augmented reality, smart factory, and autonomous driving. Among the positioning techniques, the positioning method using beacons has been considered a challenging task due to the deviation of the RSSI value. In this study, the position of a moving object is predicted by training a neural network that takes the RSSI value of the receiver as an input and the distance as the target value. To do this, the measured distance versus RSSI was collected. A neural network was introduced to create synthetic data from the collected actual data. Based on this neural network, the RSSI value versus distance was predicted. The real value of RSSI was obtained as a neural network for generating synthetic data, and based on this value, the coordinates of the object were estimated by learning a neural network that tracks the location of a terminal in a virtual environment.
Keywords
Indoor localization; Beacon; BLE; Neural network;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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