Semi-Supervised Learning Framework Improves Wi-Fi-Based Indoor Localization
Researchers have proposed a Mean Teacher-based semi-supervised learning (SSL) framework for Wi-Fi RSSI fingerprinting that significantly improves indoor localization accuracy. The approach addresses longstanding challenges of labor-intensive labeled data collection and poor model generalization in dynamic environments by leveraging both labeled and unlabeled data. The framework achieves up to 49.2% reduction in mean positioning error under dynamic conditions, suggesting practical value for large-scale indoor navigation systems.
A research paper accepted in Applied Soft Computing presents a semi-supervised learning framework for deep neural network-based indoor localization using Wi-Fi Received Signal Strength Indicator (RSSI) fingerprinting. Traditional approaches rely heavily on supervised learning, which requires extensive labeled data collection and struggles to adapt to environmental changes over time. The proposed system is built on the Mean Teacher architecture, which generates stable pseudo-labels via exponential moving average of model weights, balancing performance and computational efficiency better than alternatives like the Pi-Model or Temporal Ensembling. The framework incorporates access point selection, model pre-training and cloning, and batch-level noise injection, and supports both offline static training and continuous online retraining using unlabeled data from deployed users. On the UJIIndoorLoc benchmark database, mean 3D localization errors were reduced by approximately 7.4% and 7.7% for the CNNLoc and SIMO-DNN models, respectively, compared to supervised learning baselines. On the XJTLU dynamic database, which simulates real-world environmental variation, the maximum reduction in mean 2D error reached 49.2%, highlighting the framework's robustness in challenging scenarios. The work was first submitted in July 2024 and published online in June 2026.
What's missing
Generalizability to environments beyond the two tested databases (UJIIndoorLoc and XJTLU) remains an open question, and comparisons with other state-of-the-art SSL methods beyond Mean Teacher, Pi-Model, and Temporal Ensembling are not provided.
What different sources said
- arXiv cs.LGCenter
Mean Teacher based SSL Framework for Indoor Localization Using Wi-Fi RSSI Fingerprinting
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