Ensemble learning model for Wifi indoor positioning systems

Doan Tinh Pham (Hanoi University of Science and Technology)
Ta Thi Ngoc Mai (Hanoi University of Science and Technology)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 10, No 1: March 2021

WiFi indoor positioning researches have received much attention from researchers recently. In this research, we focus on studying the performance of indoor positioning systems that utilize our new proposed ensemble machine learning model. Our new ensemble learning model uses several models for normal data training and position prediction, then it uses the verification data together with its' prediction errors from trained models as the input data to train an intermediate classification model to classify which set of Wifi received signal strength indicator (RSSI) is the best match for each position prediction model. The experimental result shows that our proposed ensemble model outperforms other compared models.