Improving Artificial Neural Network Indoor Positioning System Accuracy using Hybrid Method

Improving Artificial Neural Network Indoor Positioning System Accuracy using Hybrid Method

© 2021 by IJETT Journal
Volume-69 Issue-11
Year of Publication : 2021
Authors : Julio Tanadi, Benfano Soewito
DOI :  10.14445/22315381/IJETT-V69I11P220

How to Cite?

Julio Tanadi, Benfano Soewito, "Improving Artificial Neural Network Indoor Positioning System Accuracy using Hybrid Method," International Journal of Engineering Trends and Technology, vol. 69, no. 11, pp. 156-160, 2021. Crossref,

The indoor positioning system is widely used nowadays, increasing the favors in indoor positioning systems significantly. The usefulness of the indoor positioning system in many aspects, such as security, item tracking, and many others, is why an indoor positioning system is widely used. Unlike the positioning system that uses satellite signals, indoor positioning systems have superiority in dealing with signal difficulties in closed environments, which is why indoor positioning systems are becoming increasingly popular. Much research looks at improving this indoor positioning system`s accuracy by using trilateration, weighted k-nearest neighbor, and artificial neural networks. The artificial neural networks were chosen compared to the weighted k-nearest neighbor and a hybrid method that combines the neighbor of the weighted k-nearest neighbor algorithm results as data for the artificial neural network. This research shows that the weighted k-nearest neighbor and artificial neural network combined as the hybrid method can significantly increase the accuracy by 25%.

Indoor Positioning System, Weighted K-Nearest Neighbor, Artificial Neural Network, Internet of Things, Indoor Localization

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