A Data Preprocessing Method for Deep Learning-Based Device-Free Localization

Deep learning models are widely used in the field of Device-Free Localization (DFL), and their performances rely heavily on the labeled dataset. In this letter, a novel data preprocessing method is proposed for exploiting the full potential of a collected dataset in DFL. Firstly, we propose to employ a pix-level multimodal representation of radio images for a fusion of both amplitude and phase features from Channel State Information (CSI). Then, a novel Conditional Generative Adversarial Network with Auxiliary Classifier (AC-GAN) model is used to generate artificial samples for further expanding the collected dataset. We further employ a regression formulation to train the Convolutional Neural Networks (CNNs) for positioning. The experimental results show that using the proposed method gains approximately 18% Root-Mean-Square-Error (RMSE) improvement over the CSI amplitude-only method and 22% over the CSI phase-only method.