Feature extraction and radar track classification for detecting UAVs in civillian airspace

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs’ service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud).

In this study, an automatic take-off and landing system (ATOLS) based on radar guidance was developed to provide day/night, all weather, automatic takeoff and landing for unmanned aerial vehicles (UAVs). The ATOLS contains a ground-based tracking radar subsystem and an airborne transponder subsystem. This X-band tracking radar can provide precise position information for UAV-control operations (transponder mode) and fire-control systems (skin mode). It provides 360 degrees of azimuth coverage and therefore can be employed for navigation applications. Its maximum tracking range is about 17 km and accuracy of altitude measurement is about 1 ft with a 50-ft decision height above ground level. To substantiate the proposed ATOLS system, a differential global positioning system (DGPS) was also developed. When a UAV at a low-elevation angle is detected and tracked by a tracking radar, multipath propagation often leads to the degradation of tracking accuracy or even cause the radar to break track. As a result, it becomes a potential risk to flight safety of the ATOLS guidance and control of UAVs. To overcome this technical difficulty, this paper proposes a solution based on optimization of radar parameters to mitigate the interference from multipath signals. The feasibility of proposed method has been experimentally proven through the flight trials of UAVs. Compared to the conventional low-elevation tracking techniques, the proposed one employs the radar signal processing, and does not consume additional hardware and resources.

This article provides a comprehensive review of the legal frameworks governing Unmanned Aerial Vehicles (UAVs), highlighting the intricate balance between fostering innovation and addressing the multifarious challenges posed by UAV integration into civilian airspace. It delves into the international legal context, analyzing the efforts of regulatory bodies like the International Civil Aviation Organization (ICAO) to harmonize UAV regulations across different jurisdictions. A detailed exploration of national regulatory frameworks in countries such as the United States, the European Union, China, and India showcases the diversity of legal approaches and the common challenges they face, including safety, privacy, and airspace management. The article further scrutinizes regulatory challenges, emphasizing the rapid pace of technological advancements in UAVs and the implications for safety, security, and privacy. Additionally, it explores the legal implications of commercial and civil UAV use, focusing on licensing, certification, and sector-specific considerations. By examining case studies and emerging technologies, the article underscores the need for adaptive, forward-looking legal frameworks that can accommodate the evolving landscape of UAV technology while ensuring public safety and privacy. This comprehensive analysis aims to contribute to the ongoing dialogue among policymakers, industry stakeholders, and legal scholars about developing coherent, effective legal structures for UAVs in an era of rapid technological change.

Unmanned Aerial Vehicles (UAV), such as quadrotors or fixed-wing aircraft, are increasingly common in airspace between 100 and 2000 meters. Extending current aviation infrastructure management operations, such as flight plan approval or aircraft tracking, to include UAV systems is difficult for multiple reasons, such as the technical challenge of tracking objects that frequently travel below the radar floor. This work explores the feasibility of using cellular data modems to achieve low-cost UAV tracking solutions for small consumer UAVs, such as *rotors, which typically have a very low-altitude flight path. We also explore the altitude at which the data modem functionality diminishes. We present a UAV tracking architecture and web service interface, as well as the empirical evidence gathered from testing the real-time capabilities and limitations of such a system.

The main purpose of this article is to build a general platform for Unmanned Aerial Vehicle (UAV) simulation especially for vision algorithms. This general simulation platform can also connect to realistic UAVs for further tests. The platform enables UAVs controlled by a variety of advanced vision algorithms to accomplish certain tasks. Unlike the previous simulation systems, which can only achieve tasks with certain drones, we are committed to build a platform to support a variety of vision based UAVs simulation platform. And if the simulation test is successful, we can apply vision algorithms to realistic UAVs' tests based on our platform with little adaption. We use open source Gazebo as a simulator and combined with ROS (Robot Operating System) to implement UAVs simulation. Besides, we build a general UAVs link interface to connect realistic UAVs with ROS. All these works construct the general simulation platform.

In the article, a taxonomic method for selecting the optimal vectorization approach for processing current images in the optical-electronic module of unmanned aerial vehicles (UAVs) was developed. The primary goal of this method is to optimize UAV navigation systems, which is particularly relevant due to the need to improve data processing efficiency in conditions of limited access to satellite navigation systems. This is critically important for ensuring high autonomy and accuracy of UAVs during the performance of various tasks. The need for such a method arose from the specific challenges faced by UAVs when operating in complex and unpredictable conditions. Analysis has shown that the use of vector images can significantly reduce the time required for information processing. This is possible due to the smaller volume of data that needs to be processed compared to traditional raster images. Reducing information processing time is critically important for the operational capability of UAVs to respond to dynamic changes in the operational environment, especially in critical situations. The taxonomic approach applied in the study allows for systematic classification and evaluation of different vectorization methods in terms of their processing speed, accuracy of results, and data retention efficiency. This enables the identification of the most suitable method for the specific operational conditions in which the UAV is used, thereby optimizing the overall system performance. Furthermore, the results of this research are significant not only in the scientific field but also in practical applications. By improving autonomous navigation systems for UAVs, the study directly contributes to enhancing their effectiveness in complex and unpredictable conditions. This is particularly relevant for missions that require a high level of precision and reliability, such as those performed by military UAVs and emergency response. Enhanced navigation capabilities allow these UAVs to perform complex maneuvers and make real-time decisions based on processed vectorized images, thereby reducing the risk of errors and increasing mission success rates. An improved vectorization method could lead to significant enhancements in UAV navigation systems, making them more resistant to failures and capable of operating in less structured environments without heavy reliance on satellite signals. This enhancement is vitally important for both military and civilian UAV applications, where operational flexibility and reliability are extremely important.

Unmanned Aerial Vehicle Technology Making them fly was the easy part. Making them useful was the next challenge. And now unmanned aerial vehicles (UAVs) are being put to the test to determine where they are the most applicable to the oil and gas industry. Companies and university researchers developing these “flying computers” believe that their sector is ready to rapidly expand, because of recent technological advancements and legislation that will open up the unmanned skies in the industry’s largest potential market by 2015: the United States. UAVs are already being used by some oil and gas companies to inspect flare stacks and track migrating wildlife and ice floes in the Arctic. In the near future, UAVs will be used as important tools to respond to oil spills and pipeline monitoring, and in offshore installation and decommissioning operations. A few years ago, the militaries of the world held a virtual monopoly on the application of UAV technology, and UAVs available to the private sector were little more than eyes in the sky with limited functionality. Today, commercial UAVs are the benefactors of miniaturized electronics, partly thanks to the smartphone industry and advanced software programs specifically designed to make sense of the different types of data that can be gathered while flying. Equipped with lasers, high-definition cameras, thermal imaging systems that can “see” at night, and an array of other sensors, advocates of UAVs claim they are not simply cheaper alternatives to fixed-wing aircraft and helicopters, but in many ways are more capable and, without question, safer to operate. Last fall, BP completed an experimental flight in Prudhoe Bay, Alaska, using a quadrotor UAV developed by Canadian-based Aeryon Labs. Quadrotor UAVs, also called quadcopters, use four rotors to lift and move the aircraft and are known for their maneuverability. At the time, US authorities were only issuing flight certificates to determine the airworthiness of UAVs without allowing commercial operations. BP completed the test run to determine how Aeryon’s UAV could inspect oilfield equipment and pipelines to assess maintenance needs.

To archive diverse kinds of goal, multiple unmanned aerial vehicles (UAVs) need to cooperate each other. Therefore, the importance of a ground station becomes more important. This paper proposes a framework of the ground station that controls multiple and hetero UAVs. The framework contains multiple UAV control modules and UI interfaces and has the flexibility for adding diverse kinds of UAVs and embedded devices such as commercial UAVs, Arduino-based UAVs, cameras and storage devices.

PurposeThe purpose of the paper is to describe the evolving regulatory structures of the civilian unmanned aerial vehicle (UAV) in India and Japan, not yet fully developed to regulate the deployment of the UAV. India and Japan are at the forefront to overhaul the respective regulatory framework to address issues of accountability, responsibility and risks associated with the deployment of UAV technologies.Design/methodology/approachIn-depth interviews are conducted both in Japan and India to gather primary data based on the snowball sampling method. The paper addresses questions such as what is the current scenario of civilian UAV deployment in India and Japan. What are the regulation structures for Civil UAV deployment and operation and how they differ in India and Japan? What are the key regulatory challenges for Civil UAV deployment in India? How regulation structure enables or inhibits the users and operators of Civil UAVs in India? What are mutual learnings concerning UAV regulations?FindingsFindings reveal that the Indian regulations address issues of responsibility by imparting values of privacy, safety, autonomy and security; Japanese regulation prefers values of trust, responsibility, safety and ownership with more freedom to experiment.Originality/valueThe study on civilian UAV regulatory framework is a new and innovative work embedded by the dimensions of responsibility and accountability from a responsible innovation perspective. The work is a new contribution to innovation literature looked at from regulatory structures. Field visits to both Japan and India enrich the study to a new elevation.

Introduction Autonomous unmanned aerial vehicles (UAVs) are considered with increasing interest in a diverse set of civil and commercial applications, such as environmental and natural disaster monitoring, border surveillance, emergency assistance, search and rescue missions, delivery of goods, and construction [208, 132, 362, 265, 434, 110, 280, 163]. Use of single or multiple UAVs as communication relays or aerial base stations for network provisioning in emergency situations and for public safety communications has been of particular interest due to UAVs fast deployment and large coverage capabilities [331, 193, 206, 429]. The deployed aerial platform size and the number of necessary platforms are key factors in determining the on-board communication system in addition to the coverage and capacity requirements [73]. Small-scale multi-rotors are of particular interest in practice due to their ease of deployment, low acquisition and maintenance costs, high maneuverability, and ability to hover. Research and development of small-scale UAVs focused initially on control issues, such as flight stability, maneuverability, and robustness, followed by designing autonomous vehicles capable of waypoint flights with minimal user intervention. Considering the limited flight time and payload capacity of these vehicles and the area sizes the aforementioned applications might span, the interest is shifting more and more toward collaborative multiple UAV systems for efficient and successful missions. The number of UAVs might be from tens to hundreds traveling over tens of meters to kilometers for different applications (see Figure 3.1 for an illustration). Wireless communication is an essential factor not only for network provisioning with UAVs but also for successful deployment of systems consisting of multiple small-scale UAVs. It is highly likely that high-performance links and connectivity in three-dimensional space will be required for several applications (e.g., monitoring certain areas) with data delivery meeting a certain quality of service demands [35].The question arises as to which wireless technology should be employed for aerial networks that consist of air-to-ground and air-to-air links, and data needs to be delivered regardless of significant height and orientation differences. Taking into account the quality of service to be satisfied over diverse links and the high node mobility, it is not yet clear as to whether networking protocols developed for ground networks are readily deployable on UAV networks.

In this paper, we proposed an accurate and robust method for absolute pose estimation with UAV (unmanned aerial vehicle) using RANSAC (random sample consensus). Because the artificial 3D control points with high accuracy are time-consuming and the small point set may lead low measuring accuracy, we designed a customized UAV to efficiently obtain mass 3D points. A light source was mounted on the UAV and used as a 3D point. The position of the 3D point was given by RTK (real-time kinematic) mounted on the UAV, and the position of the corresponding 2D point was given by feature extraction. The 2D–3D point correspondences exhibited some outliers because of the failure of feature extraction, the error of RTK, and wrong matches. Hence, RANSAC was used to remove the outliers and obtain the coarse pose. Then, we proposed a method to refine the coarse pose, whose procedure was formulated as the optimization of a cost function about the reprojection error based on the error transferring model and gradient descent to refine it. Before that, normalization was given for all the valid 2D–3D point correspondences to improve the estimation accuracy. In addition, we manufactured a prototype of a UAV with RTK and light source to obtain mass 2D–3D point correspondences for real images. Lastly, we provided a thorough test using synthetic data and real images, compared with several state-of-the-art perspective-n-point solvers. Experimental results showed that, even with a high outlier ratio, our proposed method had better performance in terms of numerical stability, noise sensitivity, and computational speed.

Abstract. Unmanned aerial vehicles (UAVs) can be used for mapping in the close range domain, combining aerial and terrestrial photogrammetry and now the emergence of affordable platforms to carry these technologies has opened up new opportunities for mapping and modeling cadastral boundaries. At the current state mainly low cost UAVs fitted with sensors are used in mapping projects with low budgets, the amount of data produced by the UAVs can be enormous hence the need for big data techniques’ and concepts. The past couple of years have witnessed the dramatic rise of low-cost UAVs fitted with high tech Lidar sensors and as such the UAVS have now reached a level of practical reliability and professionalism which allow the use of these systems as mapping platforms. UAV based mapping provides not only the required accuracy with respect to cadastral laws and policies as well as requirements for feature extraction from the data sets and maps produced, UAVs are also competitive to other measurement technologies in terms of economic aspects. In the following an overview on how the various technologies of UAVs, big data concepts and lidar sensor technologies can work together to revolutionize cadastral mapping particularly in Africa and as a test case Botswana in particular will be used to investigate these technologies. These technologies can be combined to efficiently provide cadastral mapping in difficult to reach areas and over large areas of land similar to the Land Administration Procedures, Capacity and Systems (LAPCAS) exercise which was recently undertaken by the Botswana government, we will show how the uses of UAVS fitted with lidar sensor and utilizing big data concepts could have reduced not only costs and time for our government but also how UAVS could have provided more detailed cadastral maps.

The geo-localization and navigation technology of unmanned aerial vehicles (UAVs) in denied environments is currently a prominent research area. Prior approaches mainly employed a two-stream network with non-shared weights to extract features from UAV and satellite images separately, followed by related modeling to obtain the response map. However, the two-stream network extracts UAV and satellite features independently. This approach significantly affects the efficiency of feature extraction and increases the computational load. To address these issues, we propose a novel coarse-to-fine one-stream network (OS-FPI). Our approach allows information exchange between UAV and satellite features during early image feature extraction. To improve the model's performance, the framework retains feature maps generated at different stages of the feature extraction process for the feature fusion network, and establishes additional connections between UAV and satellite feature maps in the feature fusion network. Additionally, the framework introduces offset prediction to further refine and optimize the model's prediction results based on the classification tasks. Our proposed model, boasts a similar inference speed to FPI while significantly reducing the number of parameters. It can achieve better performance with fewer parameters under the same conditions. As a one-stream network, our proposed model not only improves inference speed, but also reduces the number of parameters. Moreover, it achieves state-of-the-art performance on the UL14 dataset. Compared to previous models, our model achieved a significant 10.92-point improvement on the RDS metric, reaching 76.25. Furthermore, its performance in meter-level localization accuracy is impressive, with 82.62% improvement in 3-meter accuracy, 64.17% improvement in 5-meter accuracy, and 37.43% improvement in 10-meter accuracy. Code and models are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cjhing/OS-FPI.</uri>

This paper introduces a virtual reality (VR)-based unmanned aerial vehicle (UAV) simulator designed to support immersive training and early-stage human factors evaluations in rescue and emergency mission contexts. The system simulates a leader-follower UAV configuration, where a human operator controls a lead drone through a VR headset and joystick, while autonomous drones maintain any geometric formations using onboard sensing and dynamic obstacle avoidance [1]. This swarm-based coordination reflects real-world search and rescue scenarios, where rapid decision-making, spatial awareness, and teamwork are essential.The simulator is developed with a focus on human-in-the-loop design, providing an immersive teleoperation experience that places the user in high-pressure environments with dynamic spatial constraints. Visual and auditory feedback, along with the ability to switch between multiple drone perspectives, is intended to support situational awareness, mission control, and error recovery in complex terrain.From a human factors perspective, the simulator serves as a flexible testbed for evaluating cognitive and ergonomic variables in safety-critical tasks. It allows the assessment of operator workload, interface usability, attention allocation, and situation awareness under realistic but controlled conditions. Subjective evaluation tools such as the NASA Task Load Index (NASA-TLX) and the Situation Awareness Rating Technique (SART) [2] can be embedded directly within the VR experience. At the same time, objective data from head and hand movement, control inputs, and UAV performance are recorded to gain insight into operator behavior.The simulator integrates high-fidelity UAV dynamics using closed-loop reference model adaptive controllers [3], and can be equipped with tools such as eye tracking, physiological sensors for workload estimation, and visual attention assessment methods like ATTENDO [4]. These additions support deeper analysis of how operators acquire information, prioritize tasks, and shift attention during mission-critical events.Overall, the VR-based UAV simulator addresses a growing need to evaluate and enhance human performance in multi-agent control systems used in emergency operations. It offers a scalable, immersive environment that supports both training and cognitive engineering. Its modular structure allows for future integration of real UAVs, digital mapping capabilities, and adaptive interfaces, making it a valuable platform for advancing human-centered UAV system design.

The commercial unmanned aerial vehicle (UAV) market is dominated by vehicles designed specifically for photography or surveillance. While relatively affordable and readily available, these UAVs have limited effectiveness in the field of emergency response due to limited sensing capabilities. These existing vehicles can be improved through sensor integration to make them applicable to emergency response. In this article, we demonstrate one example of altering an existing commercial-off-the-shelf UAV to assess disasters involving radiation by combining radiation detection, vision, and a global positioning system (GPS). The challenges associated with using low cost commercial UAVs in disaster assessment, including data transmission range, endurance, payload, and control range are also discussed.