Wu, Z.; Hu, P.; Liu, S.; Pang, T. Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System. Sensors2024, 24, 1398.
Wu, Z.; Hu, P.; Liu, S.; Pang, T. Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System. Sensors 2024, 24, 1398.
Wu, Z.; Hu, P.; Liu, S.; Pang, T. Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System. Sensors2024, 24, 1398.
Wu, Z.; Hu, P.; Liu, S.; Pang, T. Attention Mechanism and LSTM Network for Fingerprint-Based Indoor Location System. Sensors 2024, 24, 1398.
Abstract
The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%.
Keywords
fingerprinting; indoor localization system; long short-term memory (LSTM); self-attention mechanism
Subject
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
