Poster: Efficient Passive Tracking using Commodity WiFi with Single-Shot Training

Indoor tracking plays a critical role in a wide range of applications, yet existing solutions based on cameras, acoustics, or radar often face challenges related to privacy, deployment cost, and environmental sensitivity. WiFi-based methods offer a promising alternative by leveraging existing infrastructure, but most current approaches are active, requiring users to carry dedicated devices—limiting practicality in everyday scenarios. Passive WiFi tracking is more user-friendly, but existing solutions typically rely on complex feature engineering, require large training datasets, and struggle to generalize across different users and environments. In this work, we introduce a novel passive tracking system that requires only a single-shot training phase. By leveraging location signature based on statistical proximity metrics derived from CSI across multiple distributed WiFi devices, our method enables accurate, scalable, and training-efficient indoor tracking.