Drones for Security and Defense Applications

A topical collection in Drones (ISSN 2504-446X).

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Editors


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Cartographic and Land Engineering Department, Higher Polytechnic School of Avila, University of Salamanca, Hornos Caleros, 50 05003 Avila, Spain
Interests: photogrammetry; laser scanning; 3D modeling; topography; cartography
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Department of Mining Technology, Topography and Structures, University of León, Avda. Astorga, s/n, 24401 Ponferrada, Spain
Interests: photogrammetry; drones; laser scanning; radiometric calibration; remote sensing; RGB-D sensors; 3D modeling; mobile mapping; metrology; verification; inspection; quality control
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Department of Mechanical and Aerospace Engineering, New Mexico State University, Las Cruces, NM 88003, USA
Interests: energy harvesting; nonlinear dynamics; vibration and control; smart materials; aeroelasticity; fluid-structure interactions; micro-/nanoelectromechanical systems (MEMS/NEMS); flight dynamics
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Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: swarm intelligence; collaborative control; collaborative guidance; collaborative decision-making planning; UAV swarm; UAV flight control and embedded system
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Department of Digital Industry Technologies, National and Kapodistrian University of Athens (NKUA), 34400 Psahna, Greece
Interests: stochastic modeling of wireless communication channels; design and performance analysis of V2X communication systems
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Department of Systems Engineering & Department of Mechanical and Aerospace Engineering, Naval Postgraduate School, Monterey, CA 93943, USA
Interests: aerospace systems; guidance; navigation and control; image processing; artificial intelligence; swarms

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Topical Collection Information

Dear Colleagues,

In recent years, drones have revolutionized the fields of security and defense. The integration of advanced technology with versatile unmanned platforms has ushered in a new era in surveillance, reconnaissance, operations, and logistics. Autonomous drones are at the forefront of this transformation, offering enhanced surveillance and protection capabilities. Mirroring advancements seen in the commercial industry, drones equipped with modern technologies, including advanced hardware and upgraded software, are poised to significantly impact the defense sector.

This collection aims to bring together pioneering research and practical insights to highlight the latest advancements, applications, and future prospects of drone technology in the field of security and defense. By fostering innovation and collaboration among academics, researchers, and industry professionals in the field of security and defense, our goal is to shape a balanced perspective on the role of drones in modern strategies. Authors are invited to submit high-quality, original research articles and review papers that have not been previously published or submitted elsewhere. It is imperative that submitted research demonstrates clear benefits to public safety and health while addressing potential risks of harm.

Submissions should be prepared according to the journal’s guidelines on “Research with a Military Purpose or Application” (https://www.mdpi.com/journal/drones/instructions#ethics).

Submission Note:

Authors submitting papers related to military purposes or applications need to determine if their research involves dual-use items. (There is a European regulation that lists all dual-use items that you can refer to.) If so, any potential dual-use research of concern (DURC) should be explained in the cover letter upon submission. MDPI adheres to the practical framework outlined in Guidance for Editors: Research, Audit, and Service Evaluations introduced by the Committee on Publication Ethics (COPE). Research that may pose a significant threat to public health or national security must be explicitly indicated in the manuscript. For these manuscripts to be considered for peer review, the benefits to the public or public health must outweigh the risks. It is crucial for authors to evaluate and anticipate potential risks of both direct and indirect harm associated with their research and address these identified risks throughout the research process and beyond, implementing measures to mitigate them.

Following the Guidelines for researchers on dual-use and misuse of research, some possible measures to mitigate the risks include:

1. Designating certain research results as confidential to prevent unintended use.
2. Designating an independent ethics adviser or ethics board associated with the research project (separate from the institutional research committee).
3. Adapting the research design, for example, by using dummy data.
4. Publishing only a portion of research results to limit potential misuse.

If there is a significant risk of misuse, authors have an obligation to report it to the relevant ethics committee within their institution.

The following ethical principles provide authors with guidance on navigating the ethical aspects of their research:

1. Principle of Damage Control: This principle emphasizes the importance of assessing and mitigating the potential negative consequences of research. Authors should estimate how their findings could be misused or cause harm, considering stakeholders such as funders, partners, and end-users.
2. Principle of Fairness: Authors must prevent their research from perpetuating biases, discrimination, stigmatization, or physical harm to any individuals and/or populations.
3. Authors are responsible for carefully handling research data, especially sensitive information relevant to military applications. Authors should establish clear strategies for data security and access control before starting their research.

Before submitting a military-related manuscript, authors should include the following statement in the manuscript's back matter:

Current research is limited to the [please insert a specific academic field, e.g., XXX], which is beneficial [share benefits and/or primary use] and does not pose a threat to public health or national security. Authors acknowledge the dual-use potential of the research involving xxx and confirm that all necessary precautions have been taken to prevent potential misuse. As an ethical responsibility, authors strictly adhere to relevant national and international laws about DURC. Authors advocate for responsible deployment, ethical considerations, regulatory compliance, and transparent reporting to mitigate misuse risks and foster beneficial outcomes.

If the paper is accepted for publication, authors must obtain dual-use approval from their institutional review board or funding agency. If such a document is unavailable, authors can refer to the above statement signed and/or stamped by the relevant institution or organization.

In cases where concerns are raised regarding potential risks associated with submitted manuscripts, the editorial office may take proactive measures to address these concerns. This may include seeking expert advice and/or requesting additional information from the authors. MDPI reserves the right to reject any submission that does not meet these requirements.

Prof. Dr. Diego González-Aguilera
Prof. Dr. Pablo Rodríguez-Gonzálvez
Prof. Dr. Abdessattar Abdelkefi
Prof. Dr. Xiwang Dong
Prof. Dr. Petros S. Bithas
Prof. Dr. Oleg Yakimenko
Prof. Dr. Andrey V. Savkin
Dr. Eben N. Broadbent
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 collection 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

  • modern drone technology
  • unmanned platforms
  • advanced surveillance
  • security and defense
  • autonomous drones

Published Papers (2 papers)

2024

20 pages, 9929 KiB  
Article
Application of Deep Reinforcement Learning to Defense and Intrusion Strategies Using Unmanned Aerial Vehicles in a Versus Game
by Chieh-Li Chen, Yu-Wen Huang and Ting-Ju Shen
Drones 2024, 8(8), 365; https://doi.org/10.3390/drones8080365 - 31 Jul 2024
Viewed by 381
Abstract
Drones are used in complex scenes in different scenarios. Efficient and effective algorithms are required for drones to track targets of interest and protect allied targets in a versus game. This study used physical models of quadcopters and scene engines to investigate the [...] Read more.
Drones are used in complex scenes in different scenarios. Efficient and effective algorithms are required for drones to track targets of interest and protect allied targets in a versus game. This study used physical models of quadcopters and scene engines to investigate the resulting performance of attacker drones and defensive drones based on deep reinforcement learning. The deep reinforcement learning network soft actor-critic was applied in association with the proposed reward and penalty functions according to the design scenario. AirSim UAV physical modeling and mission scenarios based on Unreal Engine were used to simultaneously train attacking and defending gaming skills for both drones, such that the required combat strategies and flight skills could be improved through a series of competition episodes. After 500 episodes of practice experience, both drones could accelerate, detour, and evade to achieve reasonably good performance with a roughly tie situation. Validation scenarios also demonstrated that the attacker–defender winning ratio also improved from 1:2 to 1.2:1, which is reasonable for drones with equal flight capabilities. Although this showed that the attacker may have an advantage in inexperienced scenarios, it revealed that the strategies generated by deep reinforcement learning networks are robust and feasible. Full article
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18 pages, 6037 KiB  
Article
Intelligent Decision-Making Algorithm for UAV Swarm Confrontation Jamming: An M2AC-Based Approach
by Runze He, Di Wu, Tao Hu, Zhifu Tian, Siwei Yang and Ziliang Xu
Drones 2024, 8(7), 338; https://doi.org/10.3390/drones8070338 - 20 Jul 2024
Viewed by 432
Abstract
Unmanned aerial vehicle (UAV) swarm confrontation jamming offers a cost-effective and long-range countermeasure against hostile swarms. Intelligent decision-making is a key factor in ensuring its effectiveness. In response to the low-timeliness problem caused by linear programming in current algorithms, this paper proposes an [...] Read more.
Unmanned aerial vehicle (UAV) swarm confrontation jamming offers a cost-effective and long-range countermeasure against hostile swarms. Intelligent decision-making is a key factor in ensuring its effectiveness. In response to the low-timeliness problem caused by linear programming in current algorithms, this paper proposes an intelligent decision-making algorithm for UAV swarm confrontation jamming based on the multi-agent actor–critic (M2AC) model. First, based on Markov games, an intelligent mathematical decision-making model is constructed to transform the confrontation jamming scenario into a symbolized mathematical problem. Second, the indicator function under this learning paradigm is designed by combining the actor–critic algorithm with Markov games. Finally, by employing a reinforcement learning algorithm with multithreaded parallel training–contrastive execution for solving the model, a Markov perfect equilibrium solution is obtained. The experimental results indicate that the algorithm based on M2AC can achieve faster training and decision-making speeds, while effectively obtaining a Markov perfect equilibrium solution. The training time is reduced to less than 50% compared to the baseline algorithm, with decision times maintained below 0.05 s across all simulation conditions. This helps alleviate the low-timeliness problem of UAV swarm confrontation jamming intelligent decision-making algorithms under highly dynamic real-time conditions, leading to more effective and efficient UAV swarm operations in various jamming and electronic warfare scenarios. Full article
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Figure 1

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