用迁移学习提升可见光定位精度，适应工厂环境变化。
Transfer Learning for VLC-based indoor Localization: Addressing Environmental Variability
- 采用迁移学习框架结合深度神经网络，提升定位鲁棒性。
- 相比传统模型，定位准确率提升47%，能耗降低32%。
- 仅需30%数据即可保持相近精度，适合工业部署。
精准的室内定位在工业环境中至关重要。可见光通信（VLC）因其高精度、低功耗和极少电磁干扰，成为有前景的解决方案。然而，光照波动和障碍物等环境变化仍带来挑战。为此，我们提出一种基于迁移学习（TL）的VLC室内定位方法。利用在BOSCH工厂采集的真实数据，该框架通过深度神经网络（DNN），使定位准确率提升47%，能耗降低32%，计算时间减少40%。所提方案在不同环境条件下具有强适应性，仅需30%的数据量即可达到相近精度，是一种成本低、可扩展的工业4.0应用方案。
原文摘要 · Abstract (English)
Accurate indoor localization is crucial in industrial environments. Visible Light Communication (VLC) has emerged as a promising solution, offering high accuracy, energy efficiency, and minimal electromagnetic interference. However, VLC-based indoor localization faces challenges due to environmental variability, such as lighting fluctuations and obstacles. To address these challenges, we propose a Transfer Learning (TL)-based approach for VLC-based indoor localization. Using real-world data collected at a BOSCH factory, the TL framework integrates a deep neural network (DNN) to improve localization accuracy by 47\%, reduce energy consumption by 32\%, and decrease computational time by 40\% compared to the conventional models. The proposed solution is highly adaptable under varying environmental conditions and achieves similar accuracy with only 30\% of the dataset, making it a cost-efficient and scalable option for industrial applications in Industry 4.0.
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