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Drones
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Drones for Security and Defense Applications
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Drones for Security and Defense Applications

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

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Editors

Prof. Dr. Diego González-Aguilera

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pablo Rodríguez-Gonzálvez

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Abdessattar Abdelkefi

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Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiwang Dong

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Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Petros S. Bithas

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Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Oleg Yakimenko

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Guest Editor
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
Prof. Dr. Andrey V. Savkin

E-Mail Website
Guest Editor
School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia
Interests: robot navigation; deployment of drones; unmanned aerial vehicles; control of wireless communication networks; control of power systems; robust control and filtering; hybrid dynamical systems; control engineering; biomedical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Eben N. Broadbent

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Guest Editor
Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32603, USA
Interests: remote sensing; forest ecology; conservation biology; airborne sensors; GatorEye; landscape simulation models; ecosystem and canopy ecology
Special Issues, Collections and Topics in MDPI journals

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 (10 papers)

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2024

23 pages, 3858 KiB  
Article
Sequential Task Allocation of More Scalable Artificial Dragonfly Swarms Considering Dubins Trajectory
by Yonggang Li, Dan Wen, Siyuan Zhang and Longjiang Li
Drones 2024, 8(10), 596; https://doi.org/10.3390/drones8100596 - 18 Oct 2024
Abstract
With the rapid advancement of UAV technology and the increasing complexity of tasks, multi-UAV systems face growing challenges in task execution. Traditional task allocation algorithms often perform poorly when dealing with issues such as local optima, slow convergence speed, and low convergence accuracy, [...] Read more.
With the rapid advancement of UAV technology and the increasing complexity of tasks, multi-UAV systems face growing challenges in task execution. Traditional task allocation algorithms often perform poorly when dealing with issues such as local optima, slow convergence speed, and low convergence accuracy, making it difficult to meet the demands for efficiency and practicality in real-world applications. To address these problems, this paper focuses on collaborative task allocation technology for multi-UAV. It proposes a collaborative task allocation strategy for multi-UAV in a multi-target environment, which comprehensively considers various complex constraints in practical application scenarios. The strategy utilizes Dubins curves for trajectory planning and constructs a multi-UAV collaborative task allocation model, with targets including the shortest total distance index, the minimum time index, and the trajectory coordination index. Each UAV is set as an artificial dragonfly by modifying the traditional dragonfly algorithm, incorporating differential evolution algorithms and their crossover, mutation, and selection operations to bring UAV swarms closer to the characteristics of biological dragonflies. The modifications can enhance the global scalability of artificial dragonfly swarms (ADSs), including wider search capacity, wider speed range, and more diverse search accuracy. Meanwhile, potential solutions with global convergence properties are stored to better support real-time adjustments to task allocation. The simulation results show that the proposed strategy can generate a conflict-free task execution scheme and plan the trajectory, which has advantages in changing the data scale of the UAV and the target and improves the reliability of the system to a certain extent. Full article
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22 pages, 1052 KiB  
Article
Multi-UAV Cooperative Target Assignment Method Based on Reinforcement Learning
by Yunlong Ding, Minchi Kuang, Heng Shi and Jiazhan Gao
Drones 2024, 8(10), 562; https://doi.org/10.3390/drones8100562 - 9 Oct 2024
Viewed by 500
Abstract
To overcome the problems of traditional distributed target allocation algorithms in terms of lack of target strategic priority, poor scalability, and robustness, this paper proposes a proximal strategy optimization algorithm that combines threat assessment and attention mechanism (TAPPO). Based on the distributed training [...] Read more.
To overcome the problems of traditional distributed target allocation algorithms in terms of lack of target strategic priority, poor scalability, and robustness, this paper proposes a proximal strategy optimization algorithm that combines threat assessment and attention mechanism (TAPPO). Based on the distributed training framework, the algorithm integrates a threat assessment and dynamic attention strategy and designs a dynamic reward function based on the current hit rate of the drone and the missile benefit ratio to improve the algorithm’s exploration ability and scalability. Through an 8vs8 multi-UAV confrontation experiment in a digital twin simulation environment, the results show that the agent using the TAPPO algorithm for target allocation defeats the state machine with an 85% winning rate and is significantly better than other current mainstream target allocation algorithms, verifying the effectiveness of the algorithm. Full article
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21 pages, 1727 KiB  
Article
Flight Plan Optimisation of Unmanned Aerial Vehicles with Minimised Radar Observability Using Action Shaping Proximal Policy Optimisation
by Ahmed Moazzam Ali, Adolfo Perrusquía, Weisi Guo and Antonios Tsourdos
Drones 2024, 8(10), 546; https://doi.org/10.3390/drones8100546 - 1 Oct 2024
Viewed by 627
Abstract
The increasing use of unmanned aerial vehicles (UAVs) is overwhelming air traffic controllers for the safe management of flights. There is a growing need for sophisticated path-planning techniques that can balance mission objectives with the imperative to minimise radar exposure and reduce the [...] Read more.
The increasing use of unmanned aerial vehicles (UAVs) is overwhelming air traffic controllers for the safe management of flights. There is a growing need for sophisticated path-planning techniques that can balance mission objectives with the imperative to minimise radar exposure and reduce the cognitive burden of air traffic controllers. This paper addresses this challenge by developing an innovative path-planning methodology based on an action-shaping Proximal Policy Optimisation (PPO) algorithm to enhance UAV navigation in radar-dense environments. The key idea is to equip UAVs, including future stealth variants, with the capability to navigate safely and effectively, ensuring their operational viability in congested radar environments. An action-shaping mechanism is proposed to optimise the path of the UAV and accelerate the convergence of the overall algorithm. Simulation studies are conducted in environments with different numbers of radars and detection capabilities. The results showcase the advantages of the proposed approach and key research directions in this field. Full article
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31 pages, 74393 KiB  
Article
Hyperspectral Sensor Management for UAS: Performance Analysis of Context-Based System Architectures for Camouflage and UXO Anomaly Detection Workflows
by Linda Eckel and Peter Stütz
Drones 2024, 8(10), 529; https://doi.org/10.3390/drones8100529 - 27 Sep 2024
Viewed by 536
Abstract
Tactical aerial reconnaissance missions using small unmanned aerial systems (UASs) have become a common scenario in the military. In particular, the detection of visually obscured objects such as camouflage materials and unexploded ordnance (UXO) is of great interest. Hyperspectral sensors, which provide detailed [...] Read more.
Tactical aerial reconnaissance missions using small unmanned aerial systems (UASs) have become a common scenario in the military. In particular, the detection of visually obscured objects such as camouflage materials and unexploded ordnance (UXO) is of great interest. Hyperspectral sensors, which provide detailed spectral information beyond the visible spectrum, are highly suitable for this type of reconnaissance mission. However, the additional spectral information places higher demands on system architectures to achieve efficient and robust data processing and object detection. To overcome these challenges, the concept of data reduction by band selection is introduced. In this paper, a specialized and robust concept of context-based hyperspectral sensor management with an implemented methodology of band selection for small and challenging UXO and camouflaged material detection is presented and evaluated with two hyperspectral datasets. For this purpose, several anomaly detectors such as LRX, NCC, HDBSCAN, and bandpass filters are introduced as part of the detection workflows and tested together with the sensor management in different system architectures. The results demonstrate how sensor management can significantly improve the detection performance for UXO compared to using all sensor bands or statistically selected bands. Furthermore, the implemented detection workflows and architectures yield strong performance results and improve the anomaly detection accuracy significantly compared to common approaches of processing hyperspectral images with a single, specialized anomaly detector. Full article
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21 pages, 5624 KiB  
Article
Intelligent Trajectory Prediction Algorithm for Hypersonic Vehicle Based on Sparse Associative Structure Model
by Furong Liu, Lina Lu, Zhiheng Zhang, Yu Xie and Jing Chen
Drones 2024, 8(9), 505; https://doi.org/10.3390/drones8090505 - 19 Sep 2024
Viewed by 558
Abstract
The Hypersonic Glide Vehicle (HGV) has become a focal point in military competitions among nations. Predicting the real-time trajectory of an HGV is of significant importance for aerospace defense interception and assessing enemy combat intentions. Existing prediction methods are limited by the need [...] Read more.
The Hypersonic Glide Vehicle (HGV) has become a focal point in military competitions among nations. Predicting the real-time trajectory of an HGV is of significant importance for aerospace defense interception and assessing enemy combat intentions. Existing prediction methods are limited by the need for large data samples and poor general applicability. To address these challenges, this paper presents a novel trajectory forecasting approach based on the Sparse Association Structure Model (SASM). The SASM can uncover the relationship among known data, transfer associative relationships to unknown data, and improve the generalization of the model. Firstly, a trajectory database is established for different maneuvering modes based on the six-degree-of-freedom motion equations and models of attack and bank angles of the HGV. Subsequently, three trajectory parameters are selected as prediction variables according to the maneuvering characteristics of the HGV. A parameters prediction model based on the SASM is then constructed to predict trajectory parameters. The SASM model demonstrates high accuracy and generalization in forecasting the trajectories of three different HGV types. Experimental results show a 50.35% reduction in prediction error and a 48.7% decrease in average processing time compared to the LSTM model, highlighting the effectiveness of the proposed method for real-time trajectory forecasting. Full article
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26 pages, 7744 KiB  
Article
The Optimal Strategies of Maneuver Decision in Air Combat of UCAV Based on the Improved TD3 Algorithm
by Xianzhong Gao, Yue Zhang, Baolai Wang, Zhihui Leng and Zhongxi Hou
Drones 2024, 8(9), 501; https://doi.org/10.3390/drones8090501 - 19 Sep 2024
Viewed by 698
Abstract
Nowadays, unmanned aerial vehicles (UAVs) pose a significant challenge to air defense systems. Unmanned combat aerial vehicles (UCAVs) have been proven to be an effective method to counter the threat of UAVs in application. Therefore, maneuver decision-making has become the crucial technology to [...] Read more.
Nowadays, unmanned aerial vehicles (UAVs) pose a significant challenge to air defense systems. Unmanned combat aerial vehicles (UCAVs) have been proven to be an effective method to counter the threat of UAVs in application. Therefore, maneuver decision-making has become the crucial technology to achieve autonomous air combat for UCAVs. In order to solve the problem of maneuver decision-making, an autonomous model of UCAVs based on the deep reinforcement learning method was proposed in this paper. Firstly, the six-degree-of-freedom (DoF) dynamic model was built in three-dimensional space, and the continuous actions of tangential overload, normal overload, and roll angle were selected as the maneuver inputs. Secondly, to improve the convergence speed for the deep reinforcement learning method, the idea of “scenario-transfer training” was introduced into the twin delayed deep deterministic (TD3) policy gradient algorithm, the results showing that the improved algorithm could cut off about 60% of the training time. Thirdly, for the “nose-to-nose turns”, which is one of the classical maneuvers for experienced pilots, the optimal maneuver generated by the proposed method was analyzed. The results showed that the maneuver strategy obtained by the proposed method was highly consistent with that made by experienced fighter pilots. This is also the first time in a public article that compared the maneuver decisions made by the deep reinforcement learning method with experienced fighter pilots. This research can provide some meaningful references to generate autonomous decision-making strategies for UCAVs. Full article
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42 pages, 25590 KiB  
Article
Quantitative Assessment of Drone Pilot Performance
by Daniela Doroftei, Geert De Cubber, Salvatore Lo Bue and Hans De Smet
Drones 2024, 8(9), 482; https://doi.org/10.3390/drones8090482 - 13 Sep 2024
Viewed by 1502
Abstract
This paper introduces a quantitative methodology for assessing drone pilot performance, aiming to reduce drone-related incidents by understanding the human factors influencing performance. The challenge lies in balancing evaluations in operationally relevant environments with those in a standardized test environment for statistical relevance. [...] Read more.
This paper introduces a quantitative methodology for assessing drone pilot performance, aiming to reduce drone-related incidents by understanding the human factors influencing performance. The challenge lies in balancing evaluations in operationally relevant environments with those in a standardized test environment for statistical relevance. The proposed methodology employs a novel virtual test environment that records not only basic flight metrics but also complex mission performance metrics, such as the video quality from a target. A group of Belgian Defence drone pilots were trained using this simulator system, yielding several practical results. These include a human-performance model linking human factors to pilot performance, an AI co-pilot providing real-time flight performance guidance, a tool for generating optimal flight trajectories, a mission planning tool for ideal pilot assignment, and a method for iterative training improvement based on quantitative input. The training results with real pilots demonstrate the methodology’s effectiveness in evaluating pilot performance for complex military missions, suggesting its potential as a valuable addition to new pilot training programs. Full article
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22 pages, 7755 KiB  
Article
Enhanced Trajectory Forecasting for Hypersonic Glide Vehicle via Physics-Embedded Neural ODE
by Shaoning Lu and Yue Qian
Drones 2024, 8(8), 377; https://doi.org/10.3390/drones8080377 - 6 Aug 2024
Viewed by 936
Abstract
Forecasting hypersonic glide vehicle (HGV) trajectories accurately is crucial for defense, but traditional methods face challenges due to the scarce real-world data and the intricate dynamics of these vehicles. Data-driven approaches based on deep learning, while having emerged in recent years, often exhibit [...] Read more.
Forecasting hypersonic glide vehicle (HGV) trajectories accurately is crucial for defense, but traditional methods face challenges due to the scarce real-world data and the intricate dynamics of these vehicles. Data-driven approaches based on deep learning, while having emerged in recent years, often exhibit limitations in predictive accuracy and long-term forecasting. Whereas, physics-informed neural networks (PINNs) offer a solution by incorporating physical laws, but they treat these laws as constraints rather than fully integrating them into the learning process. This paper presents PhysNODE, a novel physics-embedded neural ODE model for the precise forecasting of HGV trajectories, which directly integrates the equations of HGV motion into a neural ODE. PhysNODE leverages a neural network to estimate the hidden aerodynamic parameters within these equations. These parameters are then combined with observable physical quantities to form a derivative function, which is fed into an ODE solver to predict the future trajectory. Comprehensive experiments using simulated datasets of HGV trajectories demonstrate that PhysNODE outperforms the state-of-the-art data-driven and physics-informed methods, particularly when training data is limited. The results highlight the benefit of embedding the physics of the HGV motion into the neural ODE for improved accuracy and stability in trajectory predicting. Full article
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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 817
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 750
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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