Wideband, Real-time Spectro-Temporal RF Identification

Abstract

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 is a challenging problem. Existing methods are limited to a small bandwidth, and lack the capability to detect and classify multiple RF emissions in every part of a wide spectrum with a unified detection and classification solution. We present WRIST, a Wideband, Real-time RF Identification system with Spectro-Temporal detection,framework and system. Our resulting deep learning (DL) 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 visual-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. WRIST's 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.