Performance Evaluation of Wi-Fi Indoor Positioning by Regression-Based Coordinate Estimation in High-Density and Multi-Device Environments

Determining the location of moving objects in indoor environments has become a crucial requirement for many application domains. While this could be vital in hospitals, it can also enhance cultural interaction in environments such as museums. It serves a variety of usage scenarios for shopping malls, museums, and airports. Fingerprinting-based location estimation, thanks to the widespread availability of Wi-Fi access points found in nearly every building, has become one of the most common solutions for indoor positioning. This study examines the performance of Wi-Fi RSSI–based regression methods for indoor localization. Using the TUJI1 multi-device dataset, six different regression models were evaluated under Euclidean distance–based performance metrics. Among these models, XGBoost regression determined the locations of moving objects with an average positioning error of 2.07 m on the test dataset and 2.04 m during training, outperforming other linear and nonlinear regression approaches. In addition, we investigated how noisy signal measurements originating from the hardware structures of different mobile devices in the test environment affect localization systems. To analyze device heterogeneity, multiple experimental scenarios were designed, including device-specific and unified models. The findings show that tree-based ensemble models provide robust and competitive performance without requiring complex deep learning architectures.