用光照频谱+GAN生成数据，实现无需基站的高精度室内定位
LiGen: GAN-Augmented Spectral Fingerprinting for Indoor Positioning
- 用光照频谱作指纹，通过GAN生成稀缺数据增强模型
- 在复杂环境中实现亚米级定位，性能比Wi-Fi系统提升50%以上
- 适合无基础设施部署场景，尤其适用于动态变化的室内环境
精准可靠的室内定位对智能建筑应用至关重要，但现有基于Wi-Fi的系统常受环境影响。本文提出新型定位系统LiGen，利用环境光的光谱强度模式作为指纹，提供更稳定且无需基础设施的替代方案。针对光谱数据有限的问题，设计基于生成对抗网络（GAN）的数据增强框架，包含两种变体：PointGAN根据坐标生成指纹，FreeGAN借助弱定位模型标注无条件样本。定位模型采用多层感知机（MLP）架构，在合成数据上训练，实现亚米级精度，较基于Wi-Fi的基线系统性能提升超过50%。LiGen在复杂环境中也表现出强鲁棒性。据我们所知，这是首个将光谱指纹与GAN数据增强结合用于室内定位的系统。
原文摘要 · Abstract (English)
Accurate and robust indoor localization is critical for smart building applications, yet existing Wi-Fi-based systems are often vulnerable to environmental conditions. This work presents a novel indoor localization system, called LiGen, that leverages the spectral intensity patterns of ambient light as fingerprints, offering a more stable and infrastructure-free alternative to radio signals. To address the limited spectral data, we design a data augmentation framework based on generative adversarial networks (GANs), featuring two variants: PointGAN, which generates fingerprints conditioned on coordinates, and FreeGAN, which uses a weak localization model to label unconditioned samples. Our positioning model, leveraging a Multi-Layer Perceptron (MLP) architecture to train on synthesized data, achieves submeter-level accuracy, outperforming Wi-Fi-based baselines by over 50\%. LiGen also demonstrates strong robustness in cluttered environments. To the best of our knowledge, this is the first system to combine spectral fingerprints with GAN-based data augmentation for indoor localization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。