HPFLRF: A High Performance Fingerprint Localization Algorithm Based on Random Forest

In recent years, indoor localization base on fingerprint has become more common. Due to the complexity and variability of indoor environment, it is difficult for traditional indoor localization algorithm to obtain better localization accuracy and stability. In this paper, we propose a high performance fingerprint localization algorithm based on random forest (HPFLRF), which has higher precision and stability. Our algorithm could select a valid subset of APs through multiple AP selection method. In addition, our algorithm uses the random forest training positioning model to improve the stability of the algorithm effectively, and overcome the problem of overfitting in single decision tree model. The results of experiment show that our algorithm has better localization performance which average positioning error is 1.3718 m, only one seventh of the localization algorithms based on multiple times AP selection and decision tree.