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Abstract:
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Smartphone positioning based on global navigation satellite systems is crucial for various applications, including navigation, emergency response, and 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. This hybrid approach combines the strengths of both model-based and data-driven methods, enhancing adaptability in complex urban settings. We further augment the measurement modeling capabilities of the GNN 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 data sets collected via smartphone receivers in urban environments and demonstrate improved performance over existing model-based and learning-based approaches.
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Video Abstract:
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