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Exploring Indoor Localization with Transformer-Based Models : A CNN-Transformer Hybrid Approach for WiFi Fingerprinting

Savin, Nicu (2023) Exploring Indoor Localization with Transformer-Based Models : A CNN-Transformer Hybrid Approach for WiFi Fingerprinting.

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Abstract:Indoor localization has become a target for many researchers due to its vast range of applications. Due to signal fading and scattering, conventional GPS-based techniques are impractical for indoor localization. However, state-of-the-art deep learning models have shown promising results in this field. The method for indoor localization presented in this research makes use of a transformer-based model and Received Signal Strength (RSS) measurements. The proposed model will be assessed in both regression tasks: predicting X and Y coordinates, and classification tasks: floor classification. The results of this research aim to contribute to the advancement of indoor localization systems by providing evidence that transformer-based models might be a good direction to follow for enhancing localization accuracy.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
Link to this item:https://purl.utwente.nl/essays/96104
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