LSTM-Attention with Multi-Sensor Fusion for High-Accuracy 3D Indoor Localization on Smartphones

While smartphone-based fingerprinting techniques have emerged as promising solutions for indoor localization, their efficacy remains constrained by suboptimal fingerprint database quality and algorithmic limitations in 2D coordinate estimation. Conventional approaches suffer from laborious data collection processes, constrained accuracy, and diminished reliability over extended periods. To address these challenges, this study proposes a novel attention-enhanced LSTM architecture synergistically integrating heterogeneous sensor data (WiFi, barometric pressure, and magnetometer) to achieve simultaneous planar localization and multi-floor identification. A dedicated foot-mounted inertial measurement system is introduced to streamline fingerprint database construction by enabling efficient sparse data acquisition through collaborative smartphone-device interactions. The developed LSTM-Attention framework demonstrates superior performance in initial position matching precision, accelerated model convergence, and enhanced trajectory consistency through adaptive feature weighting. Comprehensive evaluations across multi-story academic and office environments reveal pedestrian localization accuracy within 1.5 meters (horizontal) and floor discrimination success rates exceeding 95%, thereby advancing the state-of-the-art in smartphone-based 3D indoor positioning systems.