Evidence map›Paper›PMID 37688105›Full record

ArticleSensors (Basel, Switzerland)2023

Drone Detection and Tracking Using RF Identification Signals.

Driss Aouladhadj, Ettien Kpre, Virginie Deniau, Aymane Kharchouf, Christophe Gransart, Christophe Gaquière

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Robustness of Deep-Learning-Based RF UAV Detectors.Sensors (Basel, Switzerland) · 2024
    Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Driss AouladhadjCOSYS-LEOST, Université Gustave Eiffel, 20 Rue Élisée Reclus, 59650 Villeneuve-d'Ascq, France.ORCID 0000-0001-5923-730X
Ettien KpreMC2 Technologies, 1 Rue Héraclès, 59493 Villeneuve-d'Ascq, France.ORCID 0000-0002-6984-8048
Virginie DeniauCOSYS-LEOST, Université Gustave Eiffel, 20 Rue Élisée Reclus, 59650 Villeneuve-d'Ascq, France.ORCID 0000-0002-8802-911X
Aymane KharchoufMC2 Technologies, 1 Rue Héraclès, 59493 Villeneuve-d'Ascq, France.ORCID 0009-0009-9541-8576
Christophe GransartCOSYS-LEOST, Université Gustave Eiffel, 20 Rue Élisée Reclus, 59650 Villeneuve-d'Ascq, France.ORCID 0000-0003-4781-8654
Christophe GaquièreMC2 Technologies, 1 Rue Héraclès, 59493 Villeneuve-d'Ascq, France.ORCID 0000-0003-3082-2489

Funding

Association Nationale de la Recherche et de la Technologie 2020/0355
6 · The paper itself

Abstract

The market for unmanned aerial systems (UASs) has grown considerably worldwide, but their ability to transmit sensitive information poses a threat to public safety. To counter these threats, authorities, and anti-drone organizations are ensuring that UASs comply with regulations, focusing on strategies to mitigate the risks associated with malicious drones. This study presents a technique for detecting drone models using identification (ID) tags in radio frequency (RF) signals, enabling the extraction of real-time telemetry data through the decoding of Drone ID packets. The system, implemented with a development board, facilitates efficient drone tracking. The results of a measurement campaign performance evaluation include maximum detection distances of 1.3 km for the Mavic Air, 1.5 km for the Mavic 3, and 3.7 km for the Mavic 2 Pro. The system accurately estimates a drone's 2D position, altitude, and speed in real time. Thanks to the decoding of telemetry packets, the system demonstrates promising accuracy, with worst-case distances between estimated and actual drone positions of 35 m for the Mavic 2 Pro, 17 m for the Mavic Air, and 15 m for the Mavic 3. In addition, there is a relative error of 14% for altitude measurements and 7% for speed measurements. The reaction times calculated to secure a vulnerable site within a 200 m radius are 1.83 min (Mavic Air), 1.03 min (Mavic 3), and 2.92 min (Mavic 2 Pro). This system is proving effective in addressing emerging concerns about drone-related threats, helping to improve public safety and security.

Indexed as

C-UASdetection systemdistance estimationdroneDrone IDdrone positionreaction timeRF signaltracking systemUAV

Identifiers

PMID37688105
PMCPMC10490811

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.