用灯光光谱做定位指纹，提升室内定位精度。
Generating Light-based Fingerprints for Indoor Localization
- 用低成本传感器捕捉灯光光谱作为位置指纹
- 合成数据使定位误差从62.9厘米降至49.3厘米
- 适合数据少的场景，尤其适用于智能建筑
高精度室内定位支撑导航、应急响应、资产追踪和智能建筑服务。现有射频方案（如Wi-Fi、RFID、UWB）易受多径衰落、干扰和覆盖不稳影响。本文探索可见光通信（VLC）这一新模态，证明低成本AS7341传感器捕获的光谱特征可作为鲁棒的位置指纹。提出两阶段框架：(i) 在真实光谱测量上训练多层感知机（MLP）；(ii) 使用TabGAN生成合成样本扩充数据集。增广后数据集将平均定位误差从62.9厘米降至49.3厘米，提升20%，仅需额外5%的数据采集成本。在包含42个参考点的U型实验室中验证，表明基于GAN的数据增强有效缓解数据稀缺问题并提升泛化能力。
原文摘要 · Abstract (English)
Accurate indoor localization underpins applications ranging from wayfinding and emergency response to asset tracking and smart-building services. Radio-frequency solutions (e.g. Wi-Fi, RFID, UWB) are widely adopted but remain vulnerable to multipath fading, interference, and uncontrollable coverage variation. We explore an orthogonal modality -- visible light communication (VLC) -- and demonstrate that the spectral signatures captured by a low-cost AS7341 sensor can serve as robust location fingerprints. We introduce a two-stage framework that (i) trains a multi-layer perceptron (MLP) on real spectral measurements and (ii) enlarges the training corpus with synthetic samples produced by TabGAN. The augmented dataset reduces the mean localization error from 62.9cm to 49.3cm -- a 20% improvement -- while requiring only 5% additional data-collection effort. Experimental results obtained on 42 reference points in a U-shaped laboratory confirm that GAN-based augmentation mitigates data-scarcity issues and enhances generalization.
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