ABC-ANN Based Indoor Position Estimation Using Preprocessed RSSI

Abstract

The widespread use of mobile devices has popularized the idea of indoor navigation. The Wi-Fi fingerprint method is emerging as an important alternative indoor positioning method for GPS usage difficulties. This study utilizes RSSI signals with three preprocessed states (raw, preprocessed with the path loss adapted, and exponential transformed) to train and test an artificial neural network (ANN). A systematic approach to the determination of neuron numbers in the hidden layers and activation functions of ANN is provided. The ANN is trained by the artificial bee colony algorithm. Five ML methods have been employed for estimation. The best performance has been achieved with ABC-ANN by the path loss adapted database with the MAE of 1.01 m. The estimation done using processed RSSI values has better performance than raw RSSI values. In addition, 33% less error occurs with the mentioned method compared to the data set source study.