Tightly Coupled Graph Neural Network and Kalman Filter for Smartphone Positioning

GNSS-based smartphone positioning is crucial for a wide array of applications, including navigation, emergency response, augmented and virtual reality. Despite significant advancements, constraints on size, weight, power consumption, and cost still pose challenges, leading to degraded accuracy in challenging urban settings. To improve smartphone positioning accuracy, we introduce a novel framework that deeply couples a Graph Neural Network (GNN) with a learnable Backpropagation Kalman Filter (BKF). This hybrid approach combines the strengths of both model-based and data-driven methods, enhancing adaptability in complex urban settings. We further augment the GNN’s measurement modeling capabilities with extended features, a novel edge creation technique, and an inductive graph learning framework. Additionally, we implement a unique backpropagation strategy that uses real-time positioning corrections to refine the performance of both the GNN and the learned Kalman filter. We validate our algorithm on real-world datasets collected using smartphone receivers in urban environments and demonstrate improved performance over existing model-based and learning-based approaches.