LiGen: GAN-Augmented Spectral Fingerprinting for Indoor Positioning

TL;DR

LiGen introduces a spectral light fingerprinting system augmented with GANs to improve indoor localization accuracy and robustness, outperforming Wi-Fi methods and functioning without infrastructure reliance.

Contribution

The paper presents the first combination of spectral light fingerprints with GAN-based data augmentation for indoor positioning, achieving submeter accuracy.

Findings

Achieves over 50% better accuracy than Wi-Fi baselines.

Demonstrates robustness in cluttered indoor environments.

Uses GANs to generate realistic spectral fingerprints conditioned on locations.

Abstract

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.