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GAN Based Data Augmentation for Indoor Localization Using Labeled and Unlabeled Data

Abstract : Machine learning techniques allow accurate indoor localization with low online complexity. However, a large amount of collected data samples is needed to properly train a deep neural network (DNN) model used for localization. In this paper, we propose to generate fake fingerprints using generative adversarial networks (GANs) based on a small amount of collected data samples. We consider an indoor scenario where collected labeled data samples are rare and insufficient to generate fake samples of a good multitude and diversity in order to provide a good localization accuracy. Thus, both labeled and unlabeled fingerprints are provided to the GAN so that more realistic fake data samples are generated. Then, a DNN model is trained on mixed dataset comprising real collected labeled and pseudo-labeled fingerprints as well as fake generated pseudo-labeled fingerprints. The data augmentation based on real measurements leads to a mean localization accuracy improvement of 9.66% in comparison to the conventional semi-supervised localization algorithm.
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Contributor : Wafa Njima Connect in order to contact the contributor
Submitted on : Friday, September 17, 2021 - 11:23:21 AM
Last modification on : Tuesday, November 16, 2021 - 1:04:03 PM
Long-term archiving on: : Saturday, December 18, 2021 - 6:35:06 PM


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  • HAL Id : hal-03347456, version 1



Wafa Njima, Marwa Chafii, Raed M Shubair. GAN Based Data Augmentation for Indoor Localization Using Labeled and Unlabeled Data. Fourth International Balkan Conference on Communications and Networking (BalkanCom 2021), Sep 2021, Novi Sad, Serbia. ⟨hal-03347456⟩



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