Enhancing Indoor Localization Accuracy through Multiple Access Point Deployment

Abstract

This study addresses the limitations of wireless local area networks in indoor localization by utilizing Extra-Trees Regression (ETR) to estimate locations based on received signal strength indicator (RSSI) values from a radio environment map (REM). We investigate how integrating numerous access points can enhance indoor localization accuracy. By constructing an extensive REM using RSSI data from various access points collected by a mobile robot in the intended interior setting, we evaluate several machine learning regression techniques. Our research pays special attention to an optimized ETR model, validated through 10-fold cross-validation and hyperparameter tuning. We quantitatively evaluate the efficiency of our suggested multi-access-point approach using root mean square error (RMSE) for REM evaluation and location error metrics for accurate localization. The results show that incorporating multiple access points significantly improves indoor localization accuracy, providing a substantial improvement over single-access-point systems when assessing interior radio frequency environments.