Spectro-Temporal RF Identification using Deep Learning
This addresses the need for managing and protecting the RF spectrum, with applications in safety and security, such as detecting drones or jammers.
The paper tackles the problem of real-time detection, classification, and localization of RF emissions in wideband spectrum, achieving 90 mean Average Precision in congested environments and classifying five technologies.
RF emissions detection, classification, and spectro-temporal localization are crucial not only for tasks relating to understanding, managing, and protecting the RF spectrum, but also for safety and security applications such as detecting intruding drones or jammers. Achieving this goal for wideband spectrum and in real-time performance is a challenging problem. We present WRIST, a Wideband, Real-time RF Identification system with Spectro-Temporal detection, framework and system. Our resulting deep learning model is capable to detect, classify, and precisely locate RF emissions in time and frequency using RF samples of 100 MHz spectrum in real-time (over 6Gbps incoming I&Q streams). Such capabilities are made feasible by leveraging a deep-learning based one-stage object detection framework, and transfer learning to a multi-channel image-based RF signals representation. We also introduce an iterative training approach which leverages synthesized and augmented RF data to efficiently build large labelled datasets of RF emissions (SPREAD). WRIST detector achieves 90 mean Average Precision even in extremely congested environment in the wild. WRIST model classifies five technologies (Bluetooth, Lightbridge, Wi-Fi, XPD, and ZigBee) and is easily extendable to others. We are making our curated and annotated dataset available to the whole community. It consists of nearly 1 million fully labelled RF emissions collected from various off-the-shelf wireless radios in a range of environments and spanning the five classes of emissions.