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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
936

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Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
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Drone Detection and Tracking Using RF Identification Signals.

Driss Aouladhadj1,2, Ettien Kpre2, Virginie Deniau1

  • 1COSYS-LEOST, Université Gustave Eiffel, 20 Rue Élisée Reclus, 59650 Villeneuve-d'Ascq, France.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary

This study introduces a radio frequency (RF) detection system to identify unmanned aerial systems (UASs) by decoding their identification (ID) tags. The system enables real-time tracking and security measures against malicious drone threats.

Keywords:
C-UASDrone IDRF signalUAVdetection systemdistance estimationdronedrone positionreaction timetracking system

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Area of Science:

  • Electrical Engineering
  • Aerospace Engineering
  • Cybersecurity

Background:

  • Growing global market for unmanned aerial systems (UASs).
  • Potential security threats posed by sensitive information transmission from UASs.
  • Need for effective strategies to mitigate risks from malicious drones.

Purpose of the Study:

  • To present a novel technique for detecting and tracking UAS models using radio frequency (RF) signals.
  • To extract real-time telemetry data by decoding Drone ID packets.
  • To enhance public safety and security against drone-related threats.

Main Methods:

  • Implementation of a detection system on a development board.
  • Utilizing radio frequency (RF) signals to identify drone identification (ID) tags.
  • Decoding Drone ID packets for real-time telemetry data extraction.

Main Results:

  • Achieved maximum detection distances of 1.3 km (Mavic Air), 1.5 km (Mavic 3), and 3.7 km (Mavic 2 Pro).
  • Accurate real-time estimation of drone 2D position, altitude (14% relative error), and speed (7% relative error).
  • Demonstrated worst-case position accuracy within 15-35 m for different drone models.

Conclusions:

  • The developed system effectively detects and tracks unmanned aerial systems (UASs) using RF signals and ID tag decoding.
  • The system provides accurate real-time telemetry data crucial for security applications.
  • This technology offers a promising solution for enhancing public safety and security against malicious drone activities.