Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Robust Low-Cost Drone Detection and Classification in Low SNR Environments

Stefan Glüge, Matthias Nyfeler, Ahmad Aghaebrahimian, Nicola Ramagnano, Christof Schüpbach

TL;DR

This paper evaluates various convolutional neural networks for their ability to detect and classify drones using spectrogram data derived from consecutive Fourier transforms of signal components, focusing on model robustness in low signal-to-noise ratio (SNR) environments, which is critical for real-world applications.

Abstract

The proliferation of drones, or unmanned aerial vehicles (UAVs), has raised significant safety concerns due to their potential misuse in activities such as espionage, smuggling, and infrastructure disruption. This paper addresses the critical need for effective drone detection and classification systems that operate independently of UAV cooperation. We evaluate various convolutional neural networks (CNNs) for their ability to detect and classify drones using spectrogram data derived from consecutive Fourier transforms of signal components. The focus is on model robustness in low signal-to-noise ratio (SNR) environments, which is critical for real-world applications. A comprehensive dataset is provided to support future model development. In addition, we demonstrate a low-cost drone detection system using a standard computer, software-defined radio (SDR) and antenna, validated through real-world field testing. On our development dataset, all models consistently achieved an average balanced classification accuracy of >= 85% at SNR > -12dB. In the field test, these models achieved an average balance accuracy of > 80%, depending on transmitter distance and antenna direction. Our contributions include: a publicly available dataset for model development, a comparative analysis of CNN for drone detection under low SNR conditions, and the deployment and field evaluation of a practical, low-cost detection system.

Robust Low-Cost Drone Detection and Classification in Low SNR Environments

TL;DR

This paper evaluates various convolutional neural networks for their ability to detect and classify drones using spectrogram data derived from consecutive Fourier transforms of signal components, focusing on model robustness in low signal-to-noise ratio (SNR) environments, which is critical for real-world applications.

Abstract

The proliferation of drones, or unmanned aerial vehicles (UAVs), has raised significant safety concerns due to their potential misuse in activities such as espionage, smuggling, and infrastructure disruption. This paper addresses the critical need for effective drone detection and classification systems that operate independently of UAV cooperation. We evaluate various convolutional neural networks (CNNs) for their ability to detect and classify drones using spectrogram data derived from consecutive Fourier transforms of signal components. The focus is on model robustness in low signal-to-noise ratio (SNR) environments, which is critical for real-world applications. A comprehensive dataset is provided to support future model development. In addition, we demonstrate a low-cost drone detection system using a standard computer, software-defined radio (SDR) and antenna, validated through real-world field testing. On our development dataset, all models consistently achieved an average balanced classification accuracy of >= 85% at SNR > -12dB. In the field test, these models achieved an average balance accuracy of > 80%, depending on transmitter distance and antenna direction. Our contributions include: a publicly available dataset for model development, a comparative analysis of CNN for drone detection under low SNR conditions, and the deployment and field evaluation of a practical, low-cost detection system.
Paper Structure (18 sections, 4 equations, 9 figures, 9 tables)

This paper contains 18 sections, 4 equations, 9 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Motivation
  4. Materials
  5. Data Acquisition
  6. Data Preparation
  7. Development Dataset
  8. Detection System Prototype
  9. Methods
  10. Model Architecture and Training
  11. Model Evaluation
  12. Visualisation of Model Embeddings
  13. Detection System Field Test
  14. Results
  15. Classification performance in the Development Dataset
  16. ...and 3 more sections

Figures (9)

  • Figure 1: Recording of drone signals in the anechoic chamber. A DJI Phantom 4 Pro drone with the DJI Phantom GL300F remote control.
  • Figure 2: Log power spectrogram and IQ data samples from the development dataset at different (\ref{['fig:input_sample_a']}-\ref{['fig:input_sample_d']})
  • Figure 3: Block diagram of the mobile drone detection system.
  • Figure 4: Detection prototype at the Zurich Lake in Rapperswil.
  • Figure 5: Experimental measurement setup at the Zurich Lake in Rapperswil. One can see the four recording positions along the wooden walkway and the detection system positioned at the lake side. Further, recordings were done at different angels of the directional antenna indicated by the arrows at the detection system.
  • ...and 4 more figures