Self-Attention-Enhanced PSW-LSTM for 3-D Indoor Pedestrian Positioning With Integrated Wi-Fi, Magnetometer, and Barometer Sensors

The performance of smartphone-based fingerprinting indoor positioning methods heavily depends on the database quality and matching algorithms, typically providing only 2D coordinates. Traditional methods also face challenges like labor-intensive data collection, limited accuracy, and reduced longterm reliability. This paper proposes a Self-Attention-Enhanced Pre-Sequence Warmup Long Short-Term Memory (PSW-LSTM) framework, integrated with WiFi, barometer, and magnetometer sensors, to achieve accurate 3D pedestrian indoor positioning. Specifically, foot-mounted positioning devices are introduced to assist smartphones in collecting sparse fingerprint data, improving database efficiency. The proposed Self-Attention-Enhanced PSW-LSTM framework improves initial matching accuracy, reduces convergence time, and enhances trajectory continuity. Experimental results demonstrate that, in typical indoor environments such as educational and office spaces, the horizontal and vertical positioning accuracy for pedestrians is better than 2 meters and 0.5 meters, significantly improving smartphone-based 3D indoor positioning performance.