RF Drone Detection System Based on a Distributed Sensor Grid With Remote Hardware-Accelerated Signal Processing

Abstract

Unmanned Aerial Vehicles (UAVs), sometimes known as drones, evolved from military to civilian applications, opening up novel perspectives in a variety of everyday services. The rapidly growing consumer interest in amateur drones equipped with high-end cameras compromises the everyday safety and privacy of people. In the literature, a variety of sensing techniques based on different physical phenomena have been proposed for drone detection. Among acoustic, optical, or radar detection systems, passive radiofrequency sensing is the only one that can identify a drone even before it takes off and additionally indicate the operator's location. A spectrogram-based method is developed and optimised in terms of computing location, resulting in the possibility of sensor grid deployment over a standard Ethernet network. The detection phase involves hardware-accelerated energy sensing to extract the data frames from the background noise. Drone presence is then identified using machine learning based solely on preamble pattern recognition, which reduces the computational effort. The presented procedure is evaluated in an isolated setting employing an open-source dataset and tuned across multiple neural network architectures. Next, the complete sensor processing chain is examined in a real-life scenario. The analytical energy detector stage reaches a margin of roughly -8.7 dB in the signal-to-noise (SNR) ratio. With 1.1 M parameters, the proposed neural network achieves 99.93% simulation accuracy in up to -9.5 dB SNR range. Even after quantization for embedded platform implementation, the device can be used as a stand-alone early intrusion detector or as part of a distributed sensor grid.INDEX TERMS Convolutional neural network, drones, field programmable gate array, software defined radio, spectrogram, surveillance, unmanned aerial vehicles This article has been accepted for publication in IEEE Access.

Real-Time Detection of Unauthorized Unmanned Aerial Vehicles Using SEB-YOLOv8s

“…However, its effectiveness in complex noise environments needs to be further investigated. Przemysław Flak et al [ 18 ] proposed an RF sensor grid-based UAV surveillance system using distributed sensor grids and a custom neural network architecture, which could be divided into three phases, including signal acquisition and hardware-accelerated time-frequency domain transform computation in a software-defined radio (SDR) device, an embedded computer for UAV presence detection, and UAV identification at the data fusion center. The unique advantage of the RF approach is that it enables early intrusion detection (identification of the drone’s launch sequence and indication of the operator’s position before take-off) and classification of the drone.…”