Next-Generation Device-Free Localization and Tracking for Evolving Industrial Needs

In this paper, we propose a practical device-free localization and tracking system for mobile objects located in enclosed spaces. The localization is performed by exploiting the RF fingerprints of the object in real time. We investigate three approaches including minimum distance (MD), k-nearest neighbors (KNN), and convolutional neural network (CNN) to obtain centimeter-level localization and tracking. We develop an experimental setup in the laboratory for the proof-of-concept of the proposed methods. This study opens new research directions in the domain of device-free localization and tracking, and offers a scalable and efficient solution for future industrial needs.