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A method for mobile device positioning using a sensor network of BLE beacons, approximation of the RSSI value and artificial neural networks
A.V. Astafiev 1, D.V. Titov 2, A.L. Zhiznyakov 1, A.A. Demidov 1

1 Murom Institute (branch), Vladimir State University named after Alexander and Nikolay Stoletovs, Murom, Russia,
2 Southwest State University, Kursk, Russia

 PDF, 1445 kB

DOI: 10.18287/2412-6179-CO-826

Pages: 277-285.

Full text of article: Russian language.
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 5×5.5 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 58.4×4.5 m room, showing more accurate results compared to similar studies.

Keywords:
indoor positioning, bluetooth low energy, Kalman filter, approximation, artificial neural network.

Citation:
Astafiev AV, Titov DV, Zhiznyakov AL, Demidov AA. A method for mobile device positioning using a sensor network of BLE beacons, approximation of the RSSI value and artificial neural networks. Computer Optics 2021; 45(2): 277-285. DOI: 10.18287/2412-6179-CO-826.

Acknowledgements:
This work was financially supported by the Ministry of Science and Higher Education of the Russian Federation under the government project VlSU GB-1187/20.

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