Device-Free Room-Level Localization With WiFi Utilizing Spatial-Frequency–Time Diversity

Device-free indoor object detection and localization are essential for the success of smart homes. Traditional vision/acoustic/radar-based approaches face operational constraints that limit their effectiveness and scalability. WiFi-based approaches have recently been a promising candidate due to their ubiquity, cost-effectiveness, and privacy-preserving nature. However, most of them show inadequate performance in typical residential settings due to the limited WiFi bandwidth and the resulting low spatial resolution. In this article, we introduce a novel system using commodity WiFi that can accurately determine the specific room where the person is, i.e., room-level localization. The system employs a novel multipath selection technique to concentrate on a limited set of multipaths predominated by the proximate motions to the device. Based on the technique, a spatial feature leveraging multiple antennas to enhance the spatial resolution is proposed for more refined detection coverage. Combining the spatial feature with time- and frequency-domain features, the system is shown to achieve an overall test accuracy of 87.63%, a true positive rate of 89.47%, and a positive predictive value of 88.51%, outperforming state-of-the-art methods by >20% and showing its potential for real-world applications.