A Multimodal Graph Fingerprinting Method for Indoor Positioning Systems

WiFi fingerprinting has been extensively studied for years to provide location estimation in indoor scenarios. Researchers have used various machine learning algorithms to match the online fingerprint to the pre-collected offline fingerprints, which have location labels, for location estimation. However, neither conventional machine learning algorithms nor modern deep neural networks explore the geometric relations between WiFi access points and the location where the fingerprint was taken. Therefore, they cannot capture the non-Euclidean nature of the WiFi fingerprint. Furthermore, prior research has indicated that fusing multiple modalities can improve positioning performance compared to using only WiFi. Therefore, this study proposes a novel Multimodal Graph Fingerprinting method for indoor positioning systems. The proposed method constructs a multimodal graph at the location of the user’s smart terminal by fusing radio frequency signals, electromagnetic field (EMF) strength, and inertial sensor measurements. A hierarchical deep graph neural network is developed to learn the relations between the multimodal graphs and their locations by capturing the features of the identities (such as MAC addresses, WiFi Received Signal Strength (RSS), and EMF data) and the topology information. Experiments on a real dataset built on a university campus demonstrate that the proposed model can achieve a median positioning error of 2.1m by fusing different modalities.