Indoor Localization Using Data Augmentation via Selective Generative Adversarial Networks

Several location-based services require accurate location information in indoor environments. Recently, it has been shown that deep neural network (DNN) based received signal strength indicator (RSSI) fingerprints achieve high localization performance with low online complexity. However, such methods require a very large amount of training data, in order to properly design and optimize the DNN model, which makes the data collection very costly. In this paper, we propose generative adversarial networks for RSSI data augmentation which generate fake RSSI data based on a small set of real collected labeled data. The developed model utilizes semi-supervised learning in order to predict the pseudo-labels of the generated RSSIs. A proper selection of the generated data is proposed in order to cover the entire considered indoor environment, and to reduce the data generation error by only selecting the most realistic fake RSSIs. Extensive numerical experiments show that the proposed data augmentation and selection scheme leads to a localization accuracy improvement of 21.69% for simulated data and 15.36% for experimental data.