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Drones
Special Issues
Detection, Identification and Tracking of UAVs and Drones
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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: 14 November 2025 | Viewed by 13812

Special Issue Editors

Prof. Dr. Richard Han

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
Dr. V.P Nguyen

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
Dr. Karthik Dantu

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
Dr. Endrowednes Kuantama

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

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Published Papers (6 papers)

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Research

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30 pages, 9702 KiB  
Article
SiamCTCA: Cross-Temporal Correlation Aggregation Siamese Network for UAV Tracking
by Qiaochu Wang, Faxue Liu, Bao Zhang, Jinghong Liu, Fang Xu and Yulong Wang
Drones 2025, 9(4), 294; https://doi.org/10.3390/drones9040294 - 10 Apr 2025
Viewed by 416
Abstract
In aerial target-tracking research, complex scenarios place extremely high demands on the precision and robustness of tracking algorithms. Although the existing target-tracking algorithms have achieved good performance in general scenarios, all of them ignore the correlation between contextual information to a certain extent, [...] Read more.
In aerial target-tracking research, complex scenarios place extremely high demands on the precision and robustness of tracking algorithms. Although the existing target-tracking algorithms have achieved good performance in general scenarios, all of them ignore the correlation between contextual information to a certain extent, and the manipulation between features exacerbates the loss of information, leading to the degradation of precision and robustness, especially in the field of UAV target tracking. In response to this, we propose a new lightweight Siamese-based tracker, SiamCTCA. Its innovative cross-temporal aggregated strategy and three feature correlation fusion networks play a key role, in which the Transformer multistage embedding achieves cross-branch information fusion with the help of the intertemporal correlation interactive vision Transformer modules to efficiently integrate different levels of features, and the feed-forward residual multidimensional fusion edge mechanism reduces information loss by introducing residuals to cope with dynamic changes in the search region; and the response significance filter aggregation network suppresses the shallow noise amplification problem of neural networks. The modules are confirmed to be effective after ablation and comparison experiments, indicating that the tracker exhibits excellent tracking performance, and with faster tracking speeds than other trackers, these can be better deployed in the field of a UAV as a platform. Full article
(This article belongs to the Special Issue Detection, Identification and Tracking of UAVs and Drones)
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23 pages, 4590 KiB  
Article
Foggy Drone Teacher: Domain Adaptive Drone Detection Under Foggy Conditions
by Guida Zheng, Benying Tan, Jingxin Wu, Xiao Qin, Yujie Li and Shuxue Ding
Drones 2025, 9(2), 146; https://doi.org/10.3390/drones9020146 - 16 Feb 2025
Viewed by 757
Abstract
With the growing use of drones, efficient detection algorithms are crucial, especially under adverse weather conditions. Most existing drone detection algorithms perform well only in clear weather, resulting in significant performance drops in foggy conditions. This study focuses on improving drone detection in [...] Read more.
With the growing use of drones, efficient detection algorithms are crucial, especially under adverse weather conditions. Most existing drone detection algorithms perform well only in clear weather, resulting in significant performance drops in foggy conditions. This study focuses on improving drone detection in foggy environments using the Mean Teacher framework for domain adaptation. The Mean Teacher framework’s performance relies on the quality of the teacher model’s pseudo-labels. To enhance the quality of the pseudo-labels from the teacher model, we introduce Foggy Drone Teacher (FDT), which includes three key components: (1) Adaptive Style and Context Augmentation to reduce domain shift and improve pseudo-label quality; (2) Simplified Domain Alignment with a novel adversarial strategy to boost domain adaptation; and (3) Progressive Domain Adaptation Training, a two-stage process that helps the teacher model produce more stable and accurate pseudo-labels. In addition, owing to the lack of publicly available data, we created Foggy Drone Dataset (FDD) to support this research. Extensive experiments show that our model achieves a 21.1-point increase in AP0.5 compared to the baseline and outperforms state-of-the-art models. This method significantly improves drone detection accuracy in foggy conditions. Full article
(This article belongs to the Special Issue Detection, Identification and Tracking of UAVs and Drones)
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26 pages, 191820 KiB  
Article
Research on Automatic Tracking and Size Estimation Algorithm of “Low, Slow and Small” Targets Based on Gm-APD Single-Photon LIDAR
by Dongfang Guo, Yanchen Qu, Xin Zhou, Jianfeng Sun, Shengwen Yin, Jie Lu and Feng Liu
Drones 2025, 9(2), 85; https://doi.org/10.3390/drones9020085 - 22 Jan 2025
Viewed by 807
Abstract
In order to solve the problem of detecting, tracking and estimating the size of “low, slow and small” targets (such as UAVs) in the air, this paper designs a single-photon LiDAR imaging system based on Geiger-mode Avalanche Photodiode (Gm-APD). It improves the Mean-Shift [...] Read more.
In order to solve the problem of detecting, tracking and estimating the size of “low, slow and small” targets (such as UAVs) in the air, this paper designs a single-photon LiDAR imaging system based on Geiger-mode Avalanche Photodiode (Gm-APD). It improves the Mean-Shift algorithm and proposes an automatic tracking method that combines the weighted centroid method to realize target extraction, and the principal component analysis (PCA) method of the adaptive rotating rectangle is realized to fit the flight attitude of the target. This method uses the target intensity and distance information provided by Gm-APD LiDAR. It addresses the problem of automatic calibration and size estimation under multiple flight attitudes. The experimental results show that the improved algorithm can automatically track the targets in different flight attitudes in real time and accurately calculate their sizes. The improved algorithm is stable in the 1250-frame tracking experiment of DJI Elf 4 UAV with a flying speed of 5 m/s and a flying distance of 100 m. Among them, the fitting error of the target is always less than 2 pixels, while the size calculation error of the target is less than 2.5 cm. This shows the remarkable advantages of Gm-APD LiDAR in detecting “low, slow and small” targets. It is of practical significance to comprehensively improve the ability of UAV detection and C-UAS systems. However, the application of this technology in complex backgrounds, especially in occlusion or multi-target tracking, still faces certain challenges. In order to realize long-distance detection, further optimizing the field of view of the Gm-APD single-photon LiDAR is still a future research direction. Full article
(This article belongs to the Special Issue Detection, Identification and Tracking of UAVs and Drones)
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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
Cited by 1 | Viewed by 2098
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)
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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
Cited by 2 | Viewed by 3072
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)
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Review

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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 12 | Viewed by 5342
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)
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