Found 19 results
Open Access
Article
30 October 2025Smart Drone Neutralization: AI Driven RF Jamming and Modulation Detection with Software Defined Radio
The increasing use of wireless technologies in many aspects of people’s lives has led to a congested electromagnetic spectrum, making it critical to manage the limited available spectrum as efficiently as possible. This is particularly important for military activities such as electronic warfare, where jamming is used to disrupt enemy communication, self-attacking drones, and surveillance drones. However, current detection methods used by armed personnel, such as optical sensors and Radio Detection and Ranging (RADAR), do not include Radio Frequency (RF) analysis, which is crucial for identifying the signals used to operate drones. To combat security vulnerabilities posed by the rogue or unidentified transmitters, RF transmitters should be detected not only by the available data content of broadcasts but also by the physical properties of the transmitters. This requires faster fingerprinting and identifying procedures that extend beyond the traditional hand-engineered methods. In this paper, RF data from the drones’ remote controller is identified and collected using Software Defined Radio (SDR), a radio that employs software to perform signal-processing tasks that were previously accomplished by hardware. A deep learning model is then provided to train and detect modulation strategies utilized in drone communication and a suitable jamming strategy. This paper overviews Unmanned Aerial Vehicles (UAV) neutralization, communication signals, and Deep Learning (DL) applications. It introduces an intelligent system for modulation detection and drone jamming using Software Defined Radio (SDR). DL approaches in these areas, alongside advancements in UAV neutralization techniques, present promising research opportunities. The primary objective is to integrate recent research themes in UAV neutralization, communication signals, and Machine Learning (ML) and DL applications, delivering a more efficient and effective solution for identifying and neutralizing drones. The proposed intelligent system for modulation detection and jamming of drones based on SDR, along with deep learning approaches, holds great potential for future research in this field.
Open Access
Article
27 October 2025Interacting Multiple Model Adaptive Robust Kalman Filter for Position Estimation for Swarm Drones under Hybrid Noise Conditions
This study evaluates the Interacting Multiple Model Adaptive Robust Kalman Filter (IMM-ARKF) for accurate position estimation in a leader-follower swarm of nine drones, consisting of one leader and eight followers following distinct trajectories. The evaluation is conducted under hybrid noise conditions combining Gaussian and Student’s t-distributions at 10%, 30%, and 50% ratios. The IMM-ARKF, which relies solely on its adaptive robust filtering mechanism, is compared with standard Interacting Multiple Model Kalman Filter (IMM-KF) and Extended Kalman Filter (IMM-EKF) methods. Simulations show that IMM-ARKF provides better accuracy, reducing root mean square error (RMSE) by up to 43.9% compared to IMM-EKF and 34.9% compared to IMM-KF across different noise conditions, due to its ability to adapt to hybrid noise. However, this improved performance comes with a computational cost, increasing processing time by up to 148% compared to IMM-EKF and 92.1% compared to IMM-KF, reflecting the complexity of its adaptive approach. These results demonstrate the effectiveness of IMM-ARKF in enhancing navigation accuracy and robustness for multi-drone systems in challenging environments.
Open Access
Article
23 September 2025A Survey on XR-Based Drone Simulation: Technologies, Applications, and Future Directions
This paper presents a comprehensive survey of Extended Reality (XR)-based drone simulation systems, encom- passing their architectures, simulation engines, physics modeling, and diverse training applications. With a particular focus on manual multirotor drone operations, this study highlights how Virtual Reality (VR) and Augmented Reality (AR) are increasingly vital for pilot training and mission rehearsal. We classify these simulators based on their hardware interfaces, spatial computing capabilities, and the integration of game and physics engines. We analyze specific platforms such as Flightmare, AirSim, DroneSim, Inzpire Mixed Reality UAV Simulator, and SimFlight XR are analyzed to illustrate various design strategies, ranging from research- grade modular frameworks to commercial training tools. In this paper, we also examine the implementation of spatial mapping and weather modeling to enhance realism in AR-based simulators. Finally, we identify critical challengesthat remain to be addressed, including user immersion, regulatory alignment, and achieving high levels of physical realism, and propose future directions in which XR-integrated drone training systems can advance.
Open Access
Article
17 September 2025An Approach to Simulation & Navigation of Autonomous Unmanned Aerial Vehicle in 3D
Drone simulation refers to the emulation of Unmanned Aerial Vehicles (UAVs) in a virtual environment, replicating real-world conditions to study and test the behavior, performance, and functionalities of drones. This paper explores the simulation of UAVs in the Unreal Engine environment using MAVProxy (Micro Air Vehicle Proxy) and the Python library DroneKit. By leveraging the computational capabilities of computers, this approach enables precise visualization and control of UAV flight dynamics in three dimensions. The use of Blueprints in Unreal Engine facilitates a cost-effective and accessible simulation process, allowing engineers and scientists to refine their UAV designs before real-world deployment. Results show the applicability of this approach vs. different environments, where an alternative approach also emerges as a viable option for visualizing textured buildings. This approach shows the power of open-source collaboration in advancing innovative solutions in the dynamic field of science and technology.
Open Access
Article
12 May 2025Drone Operation with Human Natural Movement
This study proposes a method for operating drones using natural human movements. The operator simply wears virtual reality (VR) goggles. An image from the drone camera was displayed on the goggles. When the operator changes the direction of his or her face, the drone changes the direction to match that of the operator. When the operator moves their head up or down, the drone rises or falls accordingly. When the operator walks in place, rather than walking, the drone moves forward. This allows the operator to control the drone as if they were walking in the air. Each of these movements was detected by the values of the acceleration and magnetic field sensors of the smartphone mounted on the VR goggles. A machine learning method was adopted to distinguish between walking and non-walking movements. Compared with operation via conventional remote control, it was observed that the remote controller performed better than the proposed approach in the early stages. However, when the participants familiarized themselves with the natural operation, these differences became relatively small. This study combined drones, VR, and machine learning. VR provides drone pilots with a sense of realism and immersion, whereas machine learning enables the use of natural movements.
Open Access
Article
07 April 2025Evaluating a Motion-Based Region Proposal Approach with Background Subtraction Methods for Small Drone Detection
The detection of drones in complex and dynamic environments poses significant challenges due to their small size and background clutter. This study aims to address these challenges by developing a motion-based pipeline that integrates background subtraction and deep learning-based classification to detect drones in video sequences. Two background subtraction methods, Mixture of Gaussians 2 (MOG2) and Visual Background Extractor (ViBe), are assessed to isolate potential drone regions in highly complex and dynamic backgrounds. These regions are then classified using the ResNet18 architecture. The Drone-vs-Bird dataset is utilized to test the algorithm, focusing on distinguishing drones from other dynamic objects such as birds, trees, and clouds. By leveraging motion-based information, the method enhances the drone detection process by reducing computational demands. Results show that ViBe achieves a recall of 0.956 and a precision of 0.078, while MOG2 achieves a recall of 0.857 and a precision of 0.034, highlighting the comparative advantages of ViBe in detecting small drones in challenging scenarios. These findings demonstrate the robustness of the proposed pipeline and its potential contribution to enhancing surveillance and security measures.
Open Access
Article
10 March 2025Leveraging Drone Technology for Precision Agriculture: A Comprehensive Case Study in Sidi Bouzid, Tunisia
The integration of drone technology in precision agriculture offers promising solutions for enhancing crop monitoring, optimizing resource management, and improving sustainability. This study investigates the application of UAV-based remote sensing in Sidi Bouzid, Tunisia, focusing on olive tree cultivation in a semi-arid environment. REMO-M professional drones equipped with RGB and multispectral sensors were deployed to collect high-resolution imagery, enabling advanced geospatial analysis. A comprehensive methodology was implemented, including precise flight planning, image processing, GIS-based mapping, and NDVI assessments to evaluate vegetation health. The results demonstrate the significant contribution of UAV imagery in generating accurate land use classifications, detecting plant health variations, and optimizing water resource distribution. NDVI analysis revealed clear distinctions in vegetation vigor, highlighting areas affected by water stress and nutrient deficiencies. Compared to traditional monitoring methods, drone-based assessments provided high spatial resolution and real-time data, facilitating early detection of agronomic issues. These findings underscore the pivotal role of UAV technology in advancing precision agriculture, particularly in semi-arid regions where climate variability poses challenges to sustainable farming. The study provides a replicable framework for integrating drone-based monitoring into agricultural decision-making, offering strategies to improve productivity, water efficiency, and environmental resilience. The research contributes to the growing body of knowledge on agricultural technology adoption in Tunisia and similar contexts, supporting data-driven approaches to climate-smart agriculture.
Open Access
Opinion
22 August 2024Medical Drones for Public Health Emergency Preparedness, Response, and Resilience: Delivering Health for All
Amid a global metacrisis of health, environmental and economic challenges, medical delivery drones (or uncrewed aerial vehicles) offer a promising method to prepare for, and rapidly respond, to future emergencies. This opinion article summarizes the current medical delivery drone landscape, evidence base, and policy implications in the context of public health emergencies, such as pandemics, natural disasters, and humanitarian crises, with a particular emphasis on the region of sub-Saharan Africa. Using a multilateral, international health policy perspective, key challenges and opportunities, such as the development of sustainable funding mechanisms, robust regulatory frameworks, and capacity building, are identified.
Open Access
Article
15 August 2024Hazardous Gas Monitoring with IoT Enabled Drone in Underground Tunnels and Cavities
Considering the healthiness of the atmosphere in mining activities (e.g., tunnelling), two of the most important parameters to be monitored are the concentration of oxygen and the presence of harmful gases such as CO2. Traditional methods for their measurement are fixed platforms and portable gas detectors carried by miners; they are incapable of recognizing sudden or short-term pollution events or correctly accounting for the spatial scarcity of gases. A UAV (Unmanned Aerial Vehicle) device capable of guaranteeing the measurement and continuous monitoring of concentrations has been designed. By using innovative technologies, it promotes digitization in the mining sector. This approach replaces current methods that, while effective at detecting and measuring environmental parameters, are slow, routine, and heavily reliant on human input. It saves productive expenses in the sector since it reduces costs compared to hiring a field technician for activities such as analysis of environmental conditions. This saving is about 110 euros daily, representing a 32% saving per working day for each mining technical responsible for environmental control. It also obtains a 3D spatial distribution of contaminants, a high sample resolution and a high sample resolution.. It reduces inspection time in mining works and the data collection time by more than 50%. The ECODRONE project constitutes a contribution to the MINE THE GAP challenge is a project financed with European funds whose line of desire aims to combine the innovation and development of SMEs or business groups from different regions of the mining, raw materials and materials sector. This program is aimed at strengthening the existing value chains and developing new industrial ones while designing new procedures, automated technologies, information and communication flows, which increase efficiency in the consumption of resources. All of the above implies integration with a circular economy and respect for European and global efficiency policies aimed at sustainability, industrial modernization, human health and the environment.
Open Access
Review
06 August 2024Considerations for Unmanned Aerial System (UAS) Beyond Visual Line of Sight (BVLOS) Operations
This paper, intended for expert and non-expert audiences, evaluates the technical and regulatory requirements for Unmanned Aerial Systems (UAS) to operate beyond visual line of sight (BVLOS) services. UAS BVLOS operations have the potential to unlock value for the industry. However, the regulatory requirements and process can be complex and challenging for UAS operators. The work explored the BVLOS regulatory regime in the UK, Europe and the US and found similarities in process and requirements covering themes like Detect and Avoid (DAA), Remote identification and Reliable Connectivity. A unifying goal across these jurisdictions is to operate BVLOS safely and securely in non-segregated airspace. However, operating BVLOS in segregated airspace as the default or routine mode could accelerate approval and adoption. The paper reviewed existing challenges, highlighting Coverage, Capacity and Redundancy as critical for UAS BVLOS Operations. The work also highlighted the crucial role of Non-terrestrial Network (NTN) assets like Satellites and HAPS (High Altitude Platform Station) since terrestrial networks (not optimised for aerial platform coverage) may not be reliable for BVLOS connectivity.
