Indoor Positioning Using Wi-Fi RTT Based on Stacked Ensemble Model

With the emergence and development of various indoor positioning technologies, Wi-Fi fingerprint-based positioning has become one of the most widely used indoor positioning technologies due to its high performance in indoor positioning systems. This paper introduces a positioning approach for Wi-Fi fingerprints based on a stacked ensemble model. In our method, support vector regression (SVR) and XGBoost algorithms are employed to construct stacked ensemble model. Our proposed method can enhance the accuracy and robustness of indoor positioning in comparison with the existing Wi-Fi fingerprint-based positioning method. Firstly, the outlier of the raw RTT range is removed, and it is calibrated using a quadratic polynomial. Then, the Wi-Fi RTT range and its standard deviation values are used as environmental features and coordinates as output labels. The training dataset is imported into the base learner SVR and XGBoost, respectively, and its predicted output is used to create a new dataset. Finally, the new dataset is used in the linear regression model of the me-ta learner to predict the final position obtained. We simulated the complex environment of indoor positioning in our experiments, compared with existing machine learning methods and existing stacked ensemble models. The experimental results show that the proposed method effectively improves positioning accuracy.