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.
Xuan Wang, Xiao-Xiang Cao, Di-Zhou Guo et al.· IEEE Transactions on Mobile...· 0 citations
Spatiotemporal fusion (STF) represents a vital solution for continuous high-resolution Earth observation. Despite the increasing abundance of historical satellite archives, most existing STF methods are constrained by a limited number of auxiliary reference images (typically one or two), leading to underutilized multitemporal information and reduced reconstruction reliability, particularly when References are compromised by cloud contamination or abrupt land-surface variations. To address these limitations, this study proposes an arbitrary-reference STF (ArtFusion) model. The architecture incorporates an efficient contrast-aware hybrid block (ECHB) for deep feature extraction, an explicit temporal information encoder (ETIE) to utilize acquisition metadata, and a multihead cross-reference attention fusion (MCAF) module designed to facilitate the integration of an arbitrary number of reference images. A comprehensive evaluation across 24 experimental cases in three representative study regions demonstrates that ArtFusion consistently outperforms four state-of-the-art (SOTA) benchmarks [multilevel feature fusion with generative adversarial network (MLFF-GAN), C-ROBOT, RealFusion, and frequency-selected differential fusion transformer (FSDFormer)]. Compared with the best-performing baseline among the four competing methods, ArtFusion achieves average improvements of 6.8% in spectral accuracy [root-mean-square error (RMSE)] and 15.3% in spatial accuracy [improved edge difference metric (iEDGE)]. Notably, high reliability is maintained across four challenging scenarios: rapid phenological changes, drastic morphological variations, highly heterogeneous landscapes, and frequent cloud contamination, while also demonstrating strong cross-regional transferability. Despite its superior performance, ArtFusion has an extremely compact structure with 0.19 million trainable parameters, only 2.2% of MLFF-GAN’s parameters. This work demonstrates the potential of leveraging multiple reference images to push the boundaries of STF accuracy, rather than merely increasing model complexity. This flexible multireference fusion scheme provides a promising pathway for robust, large-scale Earth observation in cloudy and dynamically changing landscapes. The source code is available at: https://github.com/Andy-cumt/ArtFusion-STF
Dizhou Guo, Qianqian Jia, Xuan Wang et al.· IEEE Transactions on Geoscie...· 0 citations
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