Semi-Supervised Learning Framework Improves Wi-Fi-Based Indoor Localization
Researchers propose a Mean Teacher-based semi-supervised learning framework to improve indoor localization systems that use Wi-Fi signal strength fingerprinting. The approach addresses limitations of traditional supervised learning by leveraging both labeled and unlabeled data during training and continuous retraining in deployed systems. The framework demonstrates significant performance improvements, reducing localization errors by up to 49% in dynamic environments.
A new semi-supervised learning (SSL) framework based on the Mean Teacher algorithm aims to overcome key challenges in Wi-Fi RSSI fingerprinting for indoor localization. Traditional supervised learning approaches require extensive labeled data collection and struggle to adapt to environmental changes. The proposed framework incorporates access point selection, model pre-training, and noise injection techniques, enabling the system to use both labeled and unlabeled data during offline training and to continuously retrain using unlabeled fingerprints from deployed systems. Testing on the UJIIndoorLoc database showed 7.4-7.7% error reduction compared to conventional supervised methods, while dynamic environment testing achieved up to 49.2% error reduction. The Mean Teacher approach was selected for its balance between performance and computational efficiency compared to alternative SSL methods.
What's missing
The study does not discuss computational requirements or latency implications for real-time deployment, practical considerations for access point selection in heterogeneous environments, or comparison with other recent deep learning approaches beyond the two baseline models tested.
What different sources said
- arXiv cs.LGCenter
Mean Teacher based SSL Framework for Indoor Localization Using Wi-Fi RSSI Fingerprinting
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