  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.
