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REVIEW article

Front. Comms. Net.
Sec. Aerial and Space Networks
Volume 5 - 2024 | doi: 10.3389/frcmn.2024.1440727
This article is part of the Research Topic UAV Systems: Security, Resource Allocation, and Applications to Wireless Communications View all articles

Machine Learning Algorithms Applied for Drone Detection and Classification: Benefits and Challenges

Provisionally accepted
Manel Mrabet Manel Mrabet 1*Maha Sliti Maha Sliti 2Lassad Ben Ammar Lassad Ben Ammar 1
  • 1 Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
  • 2 Ecole Supérieure des Communications de Tunis, Université de Carthage, Ariana, Tunisia

The final, formatted version of the article will be published soon.

    In recent years, the increasing use of drones for both commercial and recreational purposes has led to heightened concerns regarding airspace safety. To address these issues, machine learning (ML) based drone detection and classification have emerged. This study explores the potential of ML-based drone classification, utilizing technologies like radar, visual, acoustic, and radiofrequency sensing systems. It undertakes a comprehensive examination of the existing literature in this domain, with a focus on various sensing modalities and their respective technological implementations. The study indicates that ML-based drone classification is promising, with numerous successful individual contributions. It is crucial to note, however, that much of the research in this field is experimental, making it difficult to compare results from various articles.There is also a noteworthy lack of reference datasets to help in the evaluation of different solutions.

    Keywords: drone detection, Drone classification, machine learning, Radar, acoustic, RF, visual, lidar

    Received: 29 May 2024; Accepted: 01 Oct 2024.

    Copyright: © 2024 Mrabet, Sliti and Ben Ammar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Manel Mrabet, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.