Determination of positioning accuracies by using fingerprint localisation and artificial neural networks

Creative Commons License

Koyuncu H.

Thermal Science, cilt.23, 2019 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23
  • Basım Tarihi: 2019
  • Doi Numarası: 10.2298/tsci180912334k
  • Dergi Adı: Thermal Science
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Artificial neural networks, Deep neural network, General regression neural network, K-nearest neighborhood, Multi-layer back propagation neural network, Multi-layer feed forward neural network, Received signal strength indicator, Single-layer feed forward neural network
  • İstanbul Gelişim Üniversitesi Adresli: Hayır

Özet

© 2019 Society of Thermal Engineers of Serbia.Fingerprint localisation technique is an effective positioning technique to determine the object locations by using radio signal strength, values in indoors. The technique is subject to big positioning errors due to challenging environmental conditions. In this paper, initially, a fingerprint localisation technique is deployed by using classical k-nearest neighborhood method to determine the unknown object locations. Additionally, several artificial neural networks, are employed, using fingerprint data, such as single-layer feed forward neural network, multi-layer feed forward neural network, multi-layer back propagation neural network, general regression neural network, and deep neural network to determine the same unknown object locations. Fingerprint database is built by received signal strength indicator 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-nearest neighborhood method and it was found that deep neural network was the best neural network technique providing the maximum positioning accuracies.