Phase-Difference-Based Single-Anchor Visible Light Positioning Enhanced by Graph Neural Networks

Abstract

Visible light positioning (VLP) is a promising indoor localization technology with strong interference immunity and easy integration with existing infrastructure. However, most existing VLP systems rely on multi-anchor deployments and Received Signal Strength (RSS), which are costly to deploy and remain sensitive to receiver pose and ambient light. To address these limitations, we propose a single-anchor LED-array localization framework based on Phase-Difference (PD) fingerprints and Graph Neural Networks (GNNs). First, to mitigate the sensitivity of RSS and the complexity of multi-anchor geometry, we introduce the first single-anchor PD LED-array localization scheme. By encoding positional information in inter-LED PDs, this design eliminates the need for multi-anchor surveying and synchronization, suppresses slow illumination drifts. Second, to handle the instability of raw measurements, we develop a robust fingerprint construction pipeline that begins with photodiode samples and performs inter-LED phase estimation, temporal unwrapping with outlier rejection, wrap-safe sine–cosine embedding, and normalized storage in a compact database. Third, to address the limitations of heuristic K-nearest-neighbor matching, we propose a query-centric GNN-based localization framework that encodes physics-aware similarity cues in node features and learns geometry-aware neighbor weights. We evaluated the proposed method in four representative indoor scenes against ten baselines. Results show that PD fingerprints consistently outperform RSS fingerprints, and the proposed GNN further improves accuracy. It achieves mean errors of 0.26 m and 0.43 m in the corridor and office–corridor scenes, respectively, and reduces the mean error by up to 56% compared with the strongest baseline. These results demonstrate a scalable and robust pathway to high-accuracy VLP with lightweight infrastructure.