3D Indoor Localization via Universal Signal Fingerprinting Powered by LSTM

Fingerprint localization is a critical method for indoor positioning that has garnered considerable attention. Traditional methods for constructing fingerprint databases and matching algorithms frequently exhibit inefficiencies and limitations, which can undermine both the accuracy and the robustness of localization systems. This paper introduces an innovative indoor pervasive localization method leveraging deep learning. We employ a hybrid system of foot-mounted positioning devices and smartphones to efficiently create a universal fingerprint database, subsequently utilizing LSTM-based deep learning methods for accurate pedestrian location matching. Experiments conducted within a standard academic building demonstrate that our proposed method can more accurately map the indoor movement trajectories of pedestrians. The localization results indicate a horizontal accuracy of 2.5 meters and a vertical accuracy of 0.2 meters. Notably, our method shows a 10% improvement in horizontal accuracy and an 18% improvement in vertical accuracy over WiFi-only approaches. Furthermore, when compared to the classical Random Forest models, our method achieves performance enhancements of 20% in horizontal and 15% in vertical accuracy.