THERMAL SCIENCE

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Hakan Koyuncu

DETERMINATION OF POSITIONING ACCURACIES BY USING FINGERPRINT LOCALISATION AND ARTIFICIAL NEURAL NETWORKS

ABSTRACT
Fingerprint localization technique is an effective positioning technique to determine the object locations by using radio signal strength (RSSI), values in indoors. The technique is subject to big positioning errors due to challenging environmental conditions. In this paper, initially, a fingerprint localization technique is deployed by using classical k-NN method to determine the unknown object locations. Additionally, several artificial neural networks, (ANN), are employed, using fingerprint data, such as SFFNN, MFFNN, MBPNN, GRNN and DNN to determine the same unknown object locations. Fingerprint database is built by RSSI measurement signatures across the grid locations. The construction and the adapted approach of different neural networks using the fingerprint data are described. The results of them are compared with the classical k-NN method and it was found that DNN was the best neural network technique providing the maximum positioning accuracies.
KEYWORDS
Received Signal Strength indicator (RSSI), k-Nearest Neighborhood (k-NN), Artificial Neural Networks (ANN), Single-Layer Feed Forward Neural Network (SFFNN) Multi-Layer Feed Forward Neural Network (MFFNN), Multi-Layer Back Propagation Neural Network (MBPNN), General Regression Neural Network (GRNN), Deep Neural Network (DNN)
PAPER SUBMITTED: 2018-09-12
PAPER REVISED: 2018-10-30
PAPER ACCEPTED: 2018-11-16
PUBLISHED ONLINE: 2018-12-16
DOI REFERENCE: https://doi.org/10.2298/TSCI180912334K
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2019, VOLUME 23, ISSUE Supplement 1, PAGES [S99 - S111]
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Determination of positioning accuracies by using fingerprint localisation and artificial neural networks

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