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| Title | Enhancing Rf Fingerprinting For Indoor Positioning Systems Using Data Augmentation |
|---|---|
| ID_Doc | 23918 |
| Authors | Junoh S.A.; Jamil S.; Pyun J.-Y. |
| Year | 2024 |
| Published | Digest of Technical Papers - IEEE International Conference on Consumer Electronics |
| DOI | http://dx.doi.org/10.1109/ICCE59016.2024.10444463 |
| Abstract | Indoor Positioning Systems (IPS) have recently emerged as a crucial technology in the Internet of Things (IoT), with widespread applications in smart cities and homes. Radio frequency-based fingerprinting, enabling location estimation through signal observations, requires manual surveys for constructing location maps. This process involves annotating radio signatures with corresponding locations, rendering it time-consuming and labor-intensive. To address this challenge, our paper proposes a data augmentation method that leverages a conditional generative adversarial network with LSTM and CNN. This approach effectively captures patterns in the training data, generating synthetic data that aligns with the distribution. Experiments in a real scenario demonstrate an average localization error of 1.966 and 1.218 m for Wi-Fi and Bluetooth low energy (BLE), surpassing traditional fingerprinting and comparable to the baseline data augmentation methods. © 2024 IEEE. |
| Author Keywords | Bluetooth low energy (BLE); data augmentation; fingerprinting localization; Generative adversarial network (GAN); Internet of Things; Wi-Fi |
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