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This article is cited in 6 scientific papers (total in 6 papers)
NUMERICAL METHODS AND DATA ANALYSIS
A method for mobile device positioning using a sensor network of BLE beacons, approximation of the RSSI value and artificial neural network
A. V. Astafieva, D. V. Titovb, A. L. Zhiznyakova, A. A. Demidova a Murom Institute (branch), Vladimir State University named after Alexander and Nikolay Stoletovs, Murom, Russia
b Southwest State University, Kursk, Russia
Abstract:
The paper considers the development of a method for positioning a mobile device using a sensor network of BLE-beacons, the approximation of RSSI values and artificial neural networks. The aim of the work is to develop a method for positioning small-scale industrial mechanization equipment for building unmanned systems for product movement tracking. The work is divided into four main parts: data synthesis, signal filtering, selection of BLE beacons, translation of the RSSI values into a distance, and multilateration. A simplified Kalman filter is proposed for filtering the input signal to suppress Gaussian noise. A description of two approaches to translating the RSSI value into a distance is given: an exponential approximation function with a coefficient of determination of 0.6994 and an artificial feedforward neural network. A comparison of the results of these approaches is carried out on several test samples: a training one, a test sample at a known distance (0–50 meters) and a test sample at an unknown distance (60–100 meters). The artificial neural network is shown to perform better in all experiments, except for the test sample at a known distance (0–50 meters), for which the r.m.s. error is higher by 0.02 m$^2$ than that for the approximation function, which can be neglected. An algorithm for positioning a mobile device based on the multilateration method is proposed. Experimental studies of the developed method have shown that the positioning error does not exceed 0.9 meters in a 5$\times$5.5 m room under monitoring. The positioning accuracy of a mobile device using the proposed method in the experiment is 40.9 % higher. Experimental studies are also conducted in a 58.4$\times$4.5 m room, showing more accurate results compared to similar studies.
Keywords:
indoor positioning, bluetooth low energy, Kalman filter, approximation, artificial neural network.
Received: 28.10.2020 Accepted: 02.02.2021
Citation:
A. V. Astafiev, D. V. Titov, A. L. Zhiznyakov, A. A. Demidov, “A method for mobile device positioning using a sensor network of BLE beacons, approximation of the RSSI value and artificial neural network”, Computer Optics, 45:2 (2021), 277–285
Linking options:
https://www.mathnet.ru/eng/co908 https://www.mathnet.ru/eng/co/v45/i2/p277
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