Detection, Identification and Tracking of UAVs and Drones

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Design and Development".

Deadline for manuscript submissions: 24 April 2025 | Viewed by 5187

Special Issue Editors


E-Mail Website
Guest Editor
School of Computing, Macquarie University, Macquarie Park, NSW 2109, Australia
Interests: mobile computing; wireless sensor networks; drone systems; operating systems; security

E-Mail Website
Guest Editor
College of Information & Computer Sciences, University of Massachusetts Amherst, Amherst, MA 01003, USA
Interests: wireless sensor networks; drone systems; ubiquitous computing; health

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Buffalo, Buffalo, NY 14260, USA
Interests: mobile sensor networks; robot networks; networked embedded systems; mobile computing; wireless networks; embedded operating systems

E-Mail Website
Guest Editor
School of Computing, Macquarie University, Sydney 2109, Australia
Interests: internet of drones; design and implementation of unmanned aerial vehicles for aerial manipulation; sensing; recognition; and path planning for autonomous drone; machine learning and data analytics; SLAM algorithms and robotics control system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of drones and UAVs has grown in importance for society in terms of sensing, monitoring, transport, and a variety of other key tasks and operations. The detection, identification, and tracking of such drones and UAVs have emerged as major needs as drones fly near airspaces where they may pose a collision threat to other aircraft, may compromise the security of a venue, or may violate the privacy of a domicile or event.  The detection of drones, at both short and long ranges, is of significant interest, as is the remote recognition and identification of the model, type, and capabilities of such drones.  The tracking of drones in the presence of other aerial distractions and interference is further emphasised.  This Special Issue seeks to attract pioneering research that advances the topics of detection, identification, and tracking of drones and UAVs through novel algorithms, techniques, tools, systems, and applications. We expect that artificial intelligence (AI) and machine learning (ML) will play major roles in future generations of advances in these topics. We welcome work exploring new sensing modalities such as vision, radar, and lidar as well as sensor fusion techniques that may accelerate the localization of drones of tracking performance. We therefore invite submissions that can provide transformative new ideas on topics including, but not limited to, dramatic improvements of the accuracy, speed, robustness, security, privacy, affordability, and scalability of drone and UAV detection, identification, and tracking.

Prof. Dr. Richard Han
Dr. V.P Nguyen
Dr. Karthik Dantu
Dr. Endrowednes Kuantama
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • detection
  • tracking
  • identification
  • sensor fusion
  • artificial intelligence
  • machine learning
  • localization

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

28 pages, 30709 KiB  
Article
Drone-Enabled AI Edge Computing and 5G Communication Network for Real-Time Coastal Litter Detection
by Sarun Duangsuwan and Phoowadon Prapruetdee
Drones 2024, 8(12), 750; https://doi.org/10.3390/drones8120750 - 12 Dec 2024
Viewed by 246
Abstract
Coastal litter is a severe environmental issue impacting marine ecosystems and coastal communities in Thailand, with plastic pollution posing one of the most urgent challenges. Every month, millions of tons of plastic waste enter the ocean, where items such as bottles, cans, and [...] Read more.
Coastal litter is a severe environmental issue impacting marine ecosystems and coastal communities in Thailand, with plastic pollution posing one of the most urgent challenges. Every month, millions of tons of plastic waste enter the ocean, where items such as bottles, cans, and other plastics can take hundreds of years to degrade, threatening marine life through ingestion, entanglement, and habitat destruction. To address this issue, we deploy drones equipped with high-resolution cameras and sensors to capture detailed coastal imagery for assessing litter distribution. This study presents the development of an AI-driven coastal litter detection system using edge computing and 5G communication networks. The AI edge server utilizes YOLOv8 and a recurrent neural network (RNN) to enable the drone to detect and classify various types of litter, such as bottles, cans, and plastics, in real-time. High-speed 5G communication supports seamless data transmission, allowing efficient monitoring. We evaluated drone performance under optimal flying heights above ground of 5 m, 7 m, and 10 m, analyzing accuracy, precision, recall, and F1-score. Results indicate that the system achieves optimal detection at an altitude of 5 m with a ground sampling distance (GSD) of 0.98 cm/pixel, yielding an F1-score of 98% for cans, 96% for plastics, and 95% for bottles. This approach facilitates real-time monitoring of coastal areas, contributing to marine ecosystem conservation and environmental sustainability. Full article
(This article belongs to the Special Issue Detection, Identification and Tracking of UAVs and Drones)
Show Figures

Figure 1

14 pages, 4235 KiB  
Article
Digital Forensic Research for Analyzing Drone and Mobile Device: Focusing on DJI Mavic 2 Pro
by Ziyu Zhao, Yongquan Wang and Genwei Liao
Drones 2024, 8(7), 281; https://doi.org/10.3390/drones8070281 - 22 Jun 2024
Viewed by 1598
Abstract
With the frequent occurrence of drone-related criminal cases, drone forensics has become a hot spot of concern. During drone-related criminal investigations, the implicated drones are often forcibly brought down, which poses significant challenges in conducting forensic analysis. In order to restore the truth [...] Read more.
With the frequent occurrence of drone-related criminal cases, drone forensics has become a hot spot of concern. During drone-related criminal investigations, the implicated drones are often forcibly brought down, which poses significant challenges in conducting forensic analysis. In order to restore the truth of criminal cases, it is necessary to extract data not only from the external TF card but also from internal chip memory in drone forensics. To address this issue, a drone data parser (DRDP) is proposed to extract internal and external data from criminal-implicated drones. In this paper, we present comprehensive forensics on the DJI Mavic 2 Pro, analyzing the main file structure and encryption model. According to its file structures, three case studies are conducted on various file types (DAT files, TXT files, and default files) to verify the effectiveness and applicability of the designed procedure. The results show that the encrypted data of the implicated drone, such as GPS information, flight time, flight altitude, flight distance, three velocity components (x, y, z) and other information can be extracted and decrypted correctly, which provides evidence for the identification of the case facts. Full article
(This article belongs to the Special Issue Detection, Identification and Tracking of UAVs and Drones)
Show Figures

Figure 1

Review

Jump to: Research

24 pages, 10240 KiB  
Review
A Survey on Vision-Based Anti Unmanned Aerial Vehicles Methods
by Bingshu Wang, Qiang Li, Qianchen Mao, Jinbao Wang, C. L. Philip Chen, Aihong Shangguan and Haosu Zhang
Drones 2024, 8(9), 518; https://doi.org/10.3390/drones8090518 - 23 Sep 2024
Cited by 2 | Viewed by 2519
Abstract
The rapid development and widespread application of Unmanned Aerial Vehicles (UAV) have raised significant concerns about safety and privacy, thus requiring powerful anti-UAV systems. This survey provides an overview of anti-UAV detection and tracking methods in recent years. Firstly, we emphasize the key [...] Read more.
The rapid development and widespread application of Unmanned Aerial Vehicles (UAV) have raised significant concerns about safety and privacy, thus requiring powerful anti-UAV systems. This survey provides an overview of anti-UAV detection and tracking methods in recent years. Firstly, we emphasize the key challenges of existing anti-UAV and delve into various detection and tracking methods. It is noteworthy that our study emphasizes the shift toward deep learning to enhance detection accuracy and tracking performance. Secondly, the survey organizes some public datasets, provides effective links, and discusses the characteristics and limitations of each dataset. Next, by analyzing current research trends, we have identified key areas of innovation, including the progress of deep learning techniques in real-time detection and tracking, multi-sensor fusion systems, and the automatic switching mechanisms that adapt to different conditions. Finally, this survey discusses the limitations and future research directions. This paper aims to deepen the understanding of innovations in anti-UAV detection and tracking methods. Hopefully our work can offer a valuable resource for researchers and practitioners involved in anti-UAV research. Full article
(This article belongs to the Special Issue Detection, Identification and Tracking of UAVs and Drones)
Show Figures

Figure 1

Back to TopTop