LocaGen: Low-Overhead Indoor Localization Through Spatial Augmentation

2025-11-22

AI Summary

Fingerprint-based indoor localization requires extensive on-site collection of labeled signal measurements, incurring high deployment costs. To address this, we propose a spatially enhanced conditional diffusion generative framework that synthesizes high-fidelity WiFi fingerprints for unobserved locations. Our method integrates density-driven location sampling, domain-informed data augmentation, and a spatially aware loss function. Crucially, it eliminates the need to collect measurements at all target locations, substantially reducing site-survey overhead. Evaluated on real-world WiFi datasets, our approach achieves localization accuracy comparable to the full-sampling baseline—even when only 70% of locations are observed—and outperforms existing generative methods by up to 28% in accuracy. The core contribution is the first incorporation of explicit spatial priors into conditional diffusion modeling, enabling physically interpretable and highly generalizable few-shot fingerprint synthesis.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements

Abstract

Indoor localization systems commonly rely on fingerprinting, which requires extensive survey efforts to obtain location-tagged signal data, limiting their real-world deployability. Recent approaches that attempt to reduce this overhead either suffer from low representation ability, mode collapse issues, or require the effort of collecting data at all target locations. We present LocaGen, a novel spatial augmentation framework that significantly reduces fingerprinting overhead by generating high-quality synthetic data at completely unseen locations. LocaGen leverages a conditional diffusion model guided by a novel spatially aware optimization strategy to synthesize realistic fingerprints at unseen locations using only a subset of seen locations. To further improve our diffusion model performance, LocaGen augments seen location data based on domain-specific heuristics and strategically selects the seen and unseen locations using a novel density-based approach that ensures robust coverage. Our extensive evaluation on a real-world WiFi fingerprinting dataset shows that LocaGen maintains the same localization accuracy even with 30% of the locations unseen and achieves up to 28% improvement in accuracy over state-of-the-art augmentation methods.

Problem

• Reduces fingerprinting overhead for indoor localization systems
• Generates synthetic data at unseen locations using spatial augmentation
• Improves localization accuracy with limited surveyed location data

Innovation

• Generates synthetic fingerprints at unseen locations
• Uses conditional diffusion model with spatial optimization
• Augments data via heuristics and density-based selection