Summary of Mean Teacher Based Ssl Framework For Indoor Localization Using Wi-fi Rssi Fingerprinting, by Sihao Li et al.
Mean Teacher based SSL Framework for Indoor Localization Using Wi-Fi RSSI Fingerprinting
by Sihao Li, Zhe Tang, Kyeong Soo Kim, Jeremy S. Smith
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop a novel semi-supervised learning framework for neural networks to enhance indoor localization performance in multi-building and multi-floor environments. The framework employs wireless access point selection, noise injection, and Mean Teacher model to leverage unlabeled fingerprints. This approach can manage hybrid in/out-sourcing databases and continually expand the fingerprint database with newly submitted unlabeled fingerprints during service. The proposed framework is evaluated using two established deep-learning models with the UJIIndoorLoc database, showing significant improvements in floor-level coordinate estimation compared to a supervised learning-based approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way for neural networks to learn from Wi-Fi signals to help people find their location indoors. They made it so that the system can use both labeled and unlabeled data to get better results. This helps with big buildings or multiple floors because it can handle lots of data and keep getting better over time. |
Keywords
* Artificial intelligence * Deep learning * Semi supervised * Supervised * Teacher model
