Graph-Based Radio Fingerprint Augmentation for Deep-Learning-Based Indoor Localization

Abstract

Indoor localization plays an essential role in enabling location-based services (LBSs) for sensor networks wherever global navigation satellite systems (GNSSs) are unreachable. For deep-learning-based indoor localization methods, radio data are extremely important for accurate fingerprinting-based localization but are often incomplete or are not temporally and spatially sufficient for data acquisition. Motivated by the goal of leveraging existing radio fingerprints to further improve localization accuracy, in this article, we propose a graph-based fingerprint augmentation method for deep-learning-based indoor localization. In this method, by modeling each reference point (RP) as a vertex of a graph and its radio data as graph signals, we develop a graph signal model where virtual RPs are introduced as missing vertices with missing radio data. Then, we explore the underlying spatial structure among all the real and virtual RPs to find the graph Laplacian with which to reconstruct the radio fingerprints by a semisupervised graph interpolation. On this basis, some deep neural networks and convolutional neural networks (DNNs and CNNs) trained by the reconstructed radio fingerprints are developed for indoor localization. Experiments on real datasets reveal the performance gain of the proposed fingerprint augmentation method in localization accuracy, showing the potential of graph-based data augmentation for deep-learning-based indoor localization.