Counter Unmanned Aerial Vehicles Research Articles

Development of Assets to Counter Unmanned Aerial Vehicles

Development of Assets to Counter Unmanned Aerial Vehicles

Feasibility Analysis of Optical UAV Detection Over Long Distances Using Robotic Telescopes

Substantial technological development has made Unmanned Aerial Vehicles (UAVs) more versatile, cheaper and accessible to the public in recent years. Alongside many positive effects and use cases, safety concerns are increasing as a plethora of incidents demonstrate the destructive potential of UAVs. To counteract this development and thus protect people and critical infrastructure, UAV detection, tracking and defence gains more and more research attention. Whereas, different drone detection technologies like RADAR, radio frequency and acoustic detection are deployed within multi-spectral systems, optical detection and imaging of approaching objects provide key information to correctly assess the situation. As reaction time is a crucial parameter for successful UAV defence, the operating distance of the optical detection system needs to be improved further. This paper presents the analysis, development and evaluation of a telescope-based UAV detection system. The system consists of a high precision mount and a telescope equipped with a camera. UAVs are detected in the captured video frames by the deep learning algorithm YOLOv4 using a modified architecture. The proposed system, which uses a f/10 telescope with a focal length of f = 2540 mm and a camera equipped with a 7.3 mm x 4.1 mm sensor, allows a significant increase of the optical detection range to more than 3 km of UAVs down to 0.3 m in diameter under daylight conditions and sufficient contrast, extending the reaction time significantly for counter UAV systems.

UAV Chasing Based on YOLOv3 and Object Tracker for Counter UAV Systems

This paper proposes a vision-based unmanned aerial vehicle (UAV) chasing system that can be embedded in a pursuer UAV (pUAV) to protect the attack from an evader UAV (eUAV). The proposed UAV chasing system consists of two parts, i.e., the UAV tracking and control signal generation parts. Combining a deep learning-based object detector, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">you only look once</i> version three (YOLOv3), and the existing object trackers, the proposed UAV tracking algorithm can improve the tracking performance of pUAV within affordable computational complexity. Then, the control signals of the pUAV are generated by utilizing the predicted bounding box area of the eUAV and the proportional-derivative control method. Following the object tracking benchmark performance criteria, various combinations of YOLOv3 and object trackers are examined and compared. From the evaluation results, the UAV tracking algorithm with the highest performance is chosen, whose average success rate and average precision rate for object tracking are 2.86% and 5.61% higher than YOLOv3, respectively. Through the field test, it is verified that the proposed UAV chasing system outperforms the YOLOv3 system in terms of bounding box misalignment (33% accuracy improvement) and computational complexity (71% reduction).

Learning Spatio-Temporal Attention Based Siamese Network for Tracking UAVs in the Wild

The popularity of unmanned aerial vehicles (UAVs) has made anti-UAV technology increasingly urgent. Object tracking, especially in thermal infrared videos, offers a promising solution to counter UAV intrusion. However, troublesome issues such as fast motion and tiny size make tracking infrared drone targets difficult and challenging. This work proposes a simple and effective spatio-temporal attention based Siamese method called SiamSTA, which performs reliable local searching and wide-range re-detection alternatively for robustly tracking drones in the wild. Concretely, SiamSTA builds a two-stage re-detection network to predict the target state using the template of first frame and the prediction results of previous frames. To tackle the challenge of small-scale UAV targets for long-range acquisition, SiamSTA imposes spatial and temporal constraints on generating candidate proposals within local neighborhoods to eliminate interference from background distractors. Complementarily, in case of target lost from local regions due to fast movement, a third stage re-detection module is introduced, which exploits valuable motion cues through a correlation filter based on change detection to re-capture targets from a global view. Finally, a state-aware switching mechanism is adopted to adaptively integrate local searching and global re-detection and take their complementary strengths for robust tracking. Extensive experiments on three anti-UAV datasets nicely demonstrate SiamSTA’s advantage over other competitors. Notably, SiamSTA is the foundation of the 1st-place winning entry in the 2nd Anti-UAV Challenge.

Swarm-based counter UAV defense system

Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising Internet-of-Things (IoT) devices for smart cities. Thanks to their mobility, agility, and onboard sensors’ customizability, UAVs have already demonstrated immense potential for numerous commercial applications. The UAVs expansion will come at the price of a dense, high-speed and dynamic traffic prone to UAVs going rogue or deployed with malicious intent. Counter UAV systems (C-UAS) are thus required to ensure their operations are safe. Existing C-UAS, which for the majority come from the military domain, lack scalability or induce collateral damages. This paper proposes a C-UAS able to intercept and escort intruders. It relies on an autonomous defense UAV swarm, capable of self-organizing their defense formation and to intercept the malicious UAV. This fully localized and GPS-free approach follows a modular design regarding the defense phases and it uses a newly developed balanced clustering to realize the intercept- and capture-formation. The resulting networked defense UAV swarm is resilient to communication losses. Finally, a prototype UAV simulator has been implemented. Through extensive simulations, we demonstrate the feasibility and performance of our approach.

Моделювання дій радіолокаційної розвідки в комплексній системі виявлення безпілотних літальних апаратів

The article discusses radar channel system integrated counter unmanned aerial vehicles [4]. As it is known, the system of complex counter includes a detection means, namely the means of radio and radar reconnaissance, electro-optical, acoustic, thermal means of detecting unmanned aerial vehicles, and paragraphs visual observation and weapons stations warfare, military laser system and heavy duty microwave radiation, firepower destruction. These means create an integrated counter unmanned aerial vehicles. For example, radar detection (radar) shows how to formalize the complex detection of unmanned aerial vehicles. A mathematical model of the radar detection is constructed based on Markov model "death and reproduction".The process of detection and identification the UAV is represented by the state graph of the channel radar detection, based on which the written system of differential equations for the probability of stay of the channel radar detection in the respective states. Found solution of the system of differential equations in the steady state. Introduced new indicators, namely: the intensity of the detecting UAV and the intensity of the correct detection of the UAV (identify aerial targets like UAVs). It is shown that the intensity of detection of the UAV depends on the probability of discovery their particular means radar (radar station), and given the intensity of the correct detection of the UAV from the characteristics of the radar detection means. A mathematical model is universal and allows to obtain the probability of detection of the UAV for any channel detection.In the article the analysis of the dependence of the probability of staging of the channel radar detection from the effective surface scattering (ESS) of the UAV and the range of detection. His given the corresponding graphs and it is concluded that the mathematical model adequately describes the discovery process of the UAV radar channel.

MODERN METHODS FOR DETECTION OF UNMANNED AERIAL VEHICLES

Most recent Unmanned Aerial Vehicle (UAV) detection methods are discussed in the article. Detection of UAV principles are pointed out during the overview. Brief advantages of each technique is covered and compared in between. Key technological limitations of each technique is pointed out and discussed. Several most recent and actual UAV threat accidents are presented with the indication of the used counter UAV systems. New upcoming threat of “Kamikaze” (selfdestructive) UAV and their detection limitations are presented. Case studies on the hybrid counter drone technology interactions are covered. In this article, important civil and military types of UAV propulsion are covered. Design features and future consumer demands, are analyzed, aiming at UAV components which are mandatory to perform a flight. Using recently published articles energy sources and thrust power plants are analyzed. UAV detection principles, that include audio signal signature analysis, aerial object video tracking, thermal heat signature analysis, radar systems, radio frequency spectrum and data packet communication detection are covered, pointing out their advantages and limitations. Conclusions are drawn taking into account future perspective of the UAV technology developments and upcoming future threats of the highest impact. Evaluation of most actual recent articles is made in order to overview weak points of the counter UAV system development techniques. Finally future UAV technology development is analyzed and main safety related threats are indicated. Slowly developing UAV components are indicated, putting more attention on possible UAV detection methods, where UAV mandatory components will not become obsolete.

Protect Your Sky: A Survey of Counter Unmanned Aerial Vehicle Systems

Recognizing the various and broad range of applications of unmanned aerial vehicles (UAVs) and unmanned aircraft systems (UAS) for personal, public and military applications, recent un-intentional malfunctions of uncontrollable UAVs or intentional attacks on them divert our attention and motivate us to devise a protection system, referred to as a counter UAV system (CUS). The CUS, also known as a counter-drone system, protects personal, commercial, public, and military facilities and areas from uncontrollable and belligerent UAVs by neutralizing or destroying them. This paper provides a comprehensive survey of the CUS to describe the key technologies of the CUS and provide sufficient information with wich to comprehend this system. The first part starts with an introduction of general UAVs and the concept of the CUS. In the second part, we provide an extensive survey of the CUS through a top-down approach: i) the platform of CUS including ground and sky platforms and related networks; ii) the architecture of the CUS consisting of sensing systems, command-and-control (C2) systems, and mitigation systems; and iii) the devices and functions with the sensors for detection-and-identification and localization-and-tracking actions and mitigators for neutralization. The last part is devoted to a survey of the CUS market with relevant challenges and future visions. From the CUS market survey, potential readers can identify the major players in a CUS industry and obtain information with which to develop the CUS industry. A broad understanding gained from the survey overall will assist with the design of a holistic CUS and inspire cross-domain research across physical layer designs in wireless communications, CUS network designs, control theory, mechanics, and computer science, to enhance counter UAV techniques further.

General Approach to Counter Unmanned Aerial Vehicles

The following study is devoted to the problem of preventing offensive missions led with small Unmanned Aerial Vehicles (UAVs). The main goal of the paper is to familiarize the reader with a general methodology for counteracting offensive UAVs. In this paper, the authors describe counter UAV approach, which includes several stages, such as: the identification of threats, the development of prevention measures, active counteracting (including: detection, tracking, identification, evaluation, decision making and counteraction), evaluation and improvement of the anti-UAV counteraction system.In the recommendations, the authors give two non-exclusive practical approaches to countering hostile UAVs: the offensive counter UAV and the defensive counter UAV, which concern wide range of different strategies from “destroy as many UAVs as possible” to “protect all high value assets from UAVs threats”.

Deep Learning on Multi Sensor Data for Counter UAV Applications-A Systematic Review.

Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat’s identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.

������ �����IJ� ���ϲ������ ˲������� �������� ������ �� �������� ²�������� ������ � ������� ������� �����������ֲ�

Recently, the number of incidents of damage of important state or military facilities by criminals using unmanned aerial vehicles has increased. The task of covering nuclear power plants in the airspace is entrusted to the forces and means of anti-aircraft missile forces of the Armed Forces of Ukraine. They are mainly designed for the destruction of large-sized unmanned aerial vehicles and at long distances. However, when criminals use small-sized unmanned aerial vehicles at short distances from a nuclear power station in order to reconnoiter the territory of nuclear power stations or damage vital centers of the station with attack unmanned aerial vehicles, anti-aircraft missile forces of the Armed Forces of Ukraine may not be effective. Therefore, military units for the protection of nuclear power plants, which are allocated from the National Guard of Ukraine, must have their own system to counter unmanned aerial vehicles.When building such a system, before putting it into operation, it is necessary to assess the ability of the system to perform tasks to counter unmanned aerial vehicles, taking into account many factors and parameters, which raises the problem of such an assessment. Therefore, in order to solve a specific problem, the article developed a model for countering unmanned aerial vehicles by the forces and means of military units to protect nuclear power plants. The developed model estimates the probability of detection and, with the help of a queuing system, the probability of destroying unmanned aerial vehicles, which can help the command of military units to protect nuclear power plants to make informed decisions on building a system to counter unmanned aerial vehicles. In the article, in order to verify the suitability of the developed model for its intended use, calculations have been performed for conditions close to the real ones.

Countering UAVs – the Mover of Research in Military Technology

Unmanned aerial vehicles (UAVs) are massively seeping into a wide range of human activities. Along with other remotely controlled or automatic devices, they have entered many aspects of human activities and industry. While the majority of researchers have been working on the construction, deployment and non-military use of UAVs, the protection against UAVs remained on the edge of their interest. Nowadays, the situation is rapidly changing. The risk of misuse of UAVs by criminals, guerrillas or terrorists has compelled authorities, scientists and defence industry to face this threat. Organisations have launched crucial infrastructure defence programs to cope with UAV threat. To solve this problem, it is necessary to develop disciplines improving the air space surveillance and UAVs elimination techniques. The substantial aspects of the UAVs detection and elimination were analysed, being supported by a number of conferences, workshops and journals articles. The contribution of the study in the Counter–UAV area consists particularly in generalisation and evaluation of the main technical issues. The aim of this paper is to emphasise the importance of developing new scientific fields for countering UAVs, and hence it is directed firstly on the scientific audience.

Overview of civilian counter drone systems

Unmanned aerial vehicles (UAV) commonly known as drones pose a significant threat to civilian and military personnel. This talk will explore several commercially available counter UAV systems developed for the civilian market. The systems discussed make use of electro-optic, acoustic, and radio frequency signatures to detect the presence of UAVs. The talk will also show some examples of acoustic, electro-optic, and radio frequency signatures measured on a few commercial drones.