Passive WiFi-Based Indoor Localization Through Multi-Domain Features

Passive indoor localization are critical for smart home intelligence services. Conventional methods using vision, acoustics, or radar face limitations in scalability and effectiveness due to their invasive nature. WiFi-based methods are emerging as a promising alternative because of the ubiquity of WiFi, its cost-efficiency, and non-intrusive manner. However, the performance of existing WiFi-based approaches in residential multi-room environments is often hampered by the limited bandwidth of standard WiFi devices. In this work, we introduce an innovative system that leverages commodity WiFi for precise human presence detection and room-level localization. Our system applies a novel multipath selection technique to focus a limited set of multipaths relevant to proximate motions to the device. We also introduce a spatial feature that capitalizes on multiple antennas, further improving spatial resolution and detection accuracy. Our method robustly integrates time, frequency, and spatial domain features for reliable room identification for human presence. The evaluations, considering real-world residential house and various walking patterns, validate the system’s robustness and high performance, achieving 87.62% test accuracy, an 88.03% test true positive rate, and an 88.74% test positive predictive value, surpassing state-of-the-art methods by over 25 %, showing its effectiveness in the real world.