An Ensemble Learning Algorithm for Indoor Localization

In this paper, we investigated the problem of localizing a smartphone with iBeacon signal strengths utilizing an ensemble learning algorithm. We built a real testing environment and examined the performance of the ensemble learning algorithm in our positioning system that outperformed any single classifier individually. We also proposed two approaches to improve the accuracy: Exponentially Weighted Moving Averages (EWMA) to deal with wireless signal fluctuation, and data augmentation for enlarging existing data volume. Further, the extent of the density received signal affecting the accuracy of localization by using different intervals was discussed. Most importantly, we tested our algorithm in a real environment. In order to combat overfitting, data balancing on training datasets in each reference point was introduced. By a series of comprehensive experiments, we have corroborated that the weighted fusion algorithm is capable of localization with high accuracy.