Types Of Unmanned Aerial Vehicles Research Articles

UAV Detection using Convolutional Neural Networks and Radio Frequency Data

Unmanned Aerial Vehicles (UAVs) are increasingly seen as significant threats to both civilian and military installations, highlighting the need for effective UAV detection systems. Among the various detection methods proposed, passive radio frequency sensing has proven particularly effective. In this study, we utilized an open radio frequency dataset to explore the use of Convolutional Neural Networks (CNNs) for UAV detection. Our comparison with traditional machine learning models, such as random forest, AdaBoost, and XGBoost, demonstrates that CNNs offer superior performance. Specifically, CNNs achieved impressive accuracy rates: 99.93% for detecting UAVs, 93.30% for classifying UAV types, and 76.43% for categorizing UAV modes. These results underscore the potential of CNNs as a powerful tool for UAV detection and classification.

Clustering based strategic 3D deployment and trajectory optimization of UAVs with A-star algorithm for enhanced disaster response

A broad spectrum of communication and information technologies is currently being investigated for their potential applications in disaster management. A high level of situational awareness, combined with a prompt and accurate response, is essential for the preservation of life during catastrophe scenarios. This study presents a novel communication strategy employing Unmanned Aerial Vehicles (UAVs) as aerial base stations for providing connectivity to the affected area. The system takes advantage of the flexibility and quick deployment characteristics of UAVs. The main focus is to determine the optimal UAV deployment along with trajectory planning to ensure connectivity in areas where conventional base stations are inaccessible. The proposed system employs two types of UAVs: cluster UAVs which act as stationary base stations and relay UAVs acting as mobile base stations. A three-step strategy is proposed to find the suitable location of cluster UAVs, optimize their height and power, and find the optimal trajectory of the relay UAVs to maximize the percentage of users served. Gaussian Mixture Model (GMM) clustering is employed to determine the optimal horizontal location of cluster UAVs. An optimization problem is framed for finding out the optimal height and power for cluster UAVs. Heuristic-based A-star algorithm is used to find out the trajectory of the relay UAVs which can efficiently minimize the overall path length while avoiding obstacles. The simulation results confirm the effectiveness of the proposed approach and demonstrate the performance enhancement by comparing it with the benchmark schemes.

Сучасні тенденції розвитку двигунів безпілотних літальних апаратів

The subject of this study is the current state and prospects for the development of the market for unmanned aerial vehicles (UAVs) and power plants. The purpose of this work was to identify trends in the development of UAV engines of various classes. Task: collection, systematization, and analysis of information on the most common engines of reconnaissance and strike UAVs of the operational-tactical level, as well as UAVs of the "barrage ammunition" class. This article provides a brief description of various types of UAV power plants, and their main advantages and disadvantages are given, which are often decisive when choosing the type of power plant for a specific UAV. It was found that a power plant with an internal combustion engine (ICE) is best suited for use in medium- and long-range UAVs.This study demonstrated that four-stroke internal combustion engines are most commonly used in reconnaissance-strike UAVs at both operational and tactical levels. The features of the design and characteristics of four-stroke internal combustion engines, which have become the most widely used UAVs, are described. It was determined that the introduction of turbocharging or increasing the pressure is the main means of forcing four-stroke internal combustion engines, which makes it possible to simultaneously achieve high specific power and high brake efficiency of the engine over a wide range of altitudes during its operation. It is shown that one of the most important tasks for a long-range UAV engine is to reduce the specific brake fuel consumption. Thus, there is a tendency to promote the use of high-powered aviation diesels in long-range UAVs. It was determined that the main requirements for UAV engines of the "barrage ammunition" class are high specific power, low cost and adaptability to mass production. This study shows that gasoline two-stroke internal combustion engines with crank-chamber scavenging are extremely widespread in UAVs of the "barrage ammunition" class. The design features and characteristics of UAV two-stroke internal combustion engines are described. The main means of ensuring high specific power of such two-stroke internal combustion engines are increasing the engine RPM, optimizing gas exchange processes, and reducing the weight of the construction by using new materials. Conclusion. The results show that depending on the UAV type, the set of engine requirements can vary significantly. Modern trends in the development of UAV engines of various classes have been determined. Potentially promising directions for increasing the efficiency of engines and for comprehensive improvement of their indicators and characteristics have been determined.

Automation of conceptual design and modification of aircraft type unmanned aerial vehicles using multidisciplinary optimization and evolutionary algorithms. Part 1: Methods and models

This paper proposes a method for selecting rational parameters for large-size aircraft-type unmanned aerial vehicles at the initial design stages using an optimization algorithm of differential evolution and numerical mathematical modeling of aerodynamic problems. The method assumes implementation of weight and aerodynamic balance in the main flight modes, it can consider aircraft-type unmanned aerial vehicles with one or two lifting surfaces, applies parallel calculations, and automatically generates a three-dimensional geometric model of the aircraft appearance based on the optimization results. A method for accelerating by more than three times the process of solving the problem of optimizing aircraft takeoff weight parameters by introducing the target function into the set of design variables is proposed and demonstrated. The results of assessing the reliability of the mathematical models used for aerodynamics and the correct calculation of the target function are presented, taking into account various constraints. A comprehensive check of the operability and effectiveness of the method were considered by solving demonstration problems by optimizing more than ten main design parameters of the appearance of two existing heavy-class unmanned aerial vehicles with known characteristics from open sources. Examples of using the optimization results to modify prototypes are provided.

METHOD OF RECOGNITION OF FPV-UAV RADIO SIGNALS FORMED ACCORDING TO CROSSFIRE AND EXPRESSLRS STANDARDS

Unmanned aerial vehicles (UAVs) with the First Person View (FPV) system are the most common type of attack UAVs used at the tactical level by the armed forces of the russian federation. Timely detection of facts of their using by the enemy and countering them are important tasks solved by units of the Ukrainian Defense Forces. An effective countermeasure way for FPV-UAVs is to create interference in their radio control channels frequency bands. To increase the radio suppression range, it is necessary to use interference matched in frequency and structure, the necessary condition for the formation of which is the presence of a priori information about the structure of the radio signal. Analysis of the available information on the construction of enemy FPV-UAVs shows that their radio control and telemetry channels are usually combined, have time division multiple access, the signals are formed according to CrossFire and ExpressLRS standards and have fixed sets of parameter values that can be used as features in recognition. The proposed FPV-UAV radio signal recognition technique is based on decision tree and minimum metric methods and uses a priori information on frequency, time and modulation parameters of radio signals. The combination of the two decision-making methods made it possible to reduce the computational complexity by eliminating operations for determining the parameters of radio signals that do not correspond to the decisions taken at the previous stages, and provides the possibility of adding new time parameters without changing the developed recognition technique. The method consists of a sequence of operations for estimation radio signal parameters, comparing them with known values, making decisions about the type of standard and command- telemetry radio line operation mode. Only one fragment with a duration of one frequency element is sufficient to recognize the FPV-UAV radio signal, which reduces the technical requirements for detection means in which the method can be used.

Robust Linear Discrete Control for a Hexacopter: Experimental Results

This paper presents a discrete-time robust linear control method for tracking a hexacopter's trajectory in the presence of external disturbances. The control of multi-rotor type unmanned aerial vehicles (UAVs) has gained considerable attention recently due to their various applications, such as crop spraying in precision agriculture. The control of UAVs requires robustness to reject disturbances and accommodate dynamic uncertainties. To achieve this goal, the robust discrete-time control action is designed in two stages. The first stage utilizes the solution of a difference Riccati-equation to guarantee system stability in an optimal sense. The second stage provides system robustness against external disturbances and uncertain dynamics. Furthermore, the Lyapunov stability theory for discrete linear systems is used to derive system asymptotic stability. Finally, experimental results of the hexacopter flight are provided to illustrate the effectiveness of the presented control law.

Research on autonomous decision-making of UAV swarm based on neural network algorithm

This article focuses on typical unmanned aerial vehicle (UAV) cluster autonomous collaborative reconnaissance mission scenarios, constructs UAV classes in Matlab, and effectively constructs UAV autonomous decision-making models using two different algorithms; Using Extreme Learning Machines (ELM) and introducing neural network methods to solve autonomous decision-making problems for unmanned aerial vehicles. Test the performance of the model under multiple neural network algorithms, basically achieving unmanned aerial vehicles to complete flight targets based on multiple sensor parameters in unknown environments, using reinforcement learning algorithms to achieve autonomous decision-making of unmanned aerial vehicles, achieving multi parameter fusion (with parameters greater than 4) decision-making, with a decision-making time of less than 100ms, which can ensure timely decision-making while also considering the rationality of the model.

Investigation of the Overall Damage Assessment Method Used for Unmanned Aerial Vehicles Subjected to Blast Waves

With the aim of investigating the assessment methodology for the overall damage effects on an unmanned aerial vehicle (UAV) subjected to a blast wave, the failure criteria for the typical UAV was formulated through an analysis of the structural strength design standards. Specifically, the shear force associated with wing failure can serve as a critical parameter for assessing the overall damage inflicted on the UAV by blast waves. According to the design load criterion of the aircraft, the shear force value corresponding to the overall failure of the typical UAV was calculated. Numerical simulations were conducted to investigate the mechanical response of UAV structures under blast wave loading generated by a 500 g explosive. Combining the critical shear force values obtained from theoretical calculations with the numerical simulation results, two distances between the explosives and the UAV that could produce different damage effects were estimated, namely 1 m and 2.5 m. Subsequently, static explosion experiments with equivalent explosive charges were performed, revealing different damage effects on a typical UAV at two specific distances. The numerical simulation results were highly consistent with the experimental observations, further validating the scientific and rational basis for using shear force as a primary parameter in assessing overall structural damage to fixed-wing UAVs.

UAV networks DoS attacks detection using artificial intelligence based on weighted machine learning

While Unmanned Aerial Vehicles (UAVs) have found applications across numerous industries, they still remain vulnerable to various cybersecurity challenges. Different types of cyberattacks target UAVs. Early detection of these cyberattacks is considered the most important step in ensuring the cybersecurity of UAVs. In this article, an artificial intelligence method based on machine learning was developed for detecting different types of Denial of Service (DoS) attacks targeting the UAV network. Initially in this work, feature selection methods are implemented to select the most important features. Then, machine learning methods are used to classify attacks. According to the conducted experiments, the proposed method outperformed others with an accuracy of 99.51 % and a prediction time of 0.1 s. Additionally, a novel dataset is used in this work, which offers several advantages. The dataset was created within a real-world environment rather than a simulated one. Furthermore, the data were collected within a 5G network.

Joint optimization of communication and mission performance for multi-UAV collaboration network: A multi-agent reinforcement learning method

In emergency rescue, target search and other mission scenarios with Unmanned Aerial Vehicles (UAVs), the Relay UAVs (RUs) and Mission UAVs (MUs) can collaborate to accomplish tasks in unknown environments. In this paper, we investigate the problem of trajectory planning and power control for the MU and RU collaboration. Firstly, considering the characteristics of multi-hop data transmission between the MU and Ground Control Station, a multi-UAV collaborative coverage model is designed. Meanwhile, a UAV control algorithm named MUTTO is proposed based on multi-agent reinforcement learning. In order to solve the problem of the unknown information about the number and locations of targets, the geographic coverage rate is used to replace the target coverage rate for decision making. Then, the reward functions of two types of UAVs are designed separately for the purpose of better cooperation. By simultaneously planning the trajectory and transmission power of the RU and MU, the mission target coverage rate and network transmission rate are maximized while the energy consumption of the UAV is minimized. Finally, numerical simulations results show that MUTTO can solve the UAV network control problem in an efficient way and has better performance than the benchmark method.

Comprehensive review of unmanned aerial vehicle application to safety mining management

The increasing interest of users in unmanned aerial vehicle (UAV) technology has developed new fields of application for it. Presently, UAVs are working in so many areas and the mining industry has shown grown interest in the use of UAVs for routine operations in surface, underground mines, and mine waste dumps. In this paper, a thorough review has been performed on the application of UAVs in mine safety management. Systematic reviews were conducted to summarize the results of over 60 papers published during the 2010-2021 period. The study offers a review of the most common applications involving mine safety, the styles of flying control, and the UAV types exploited. The paper also contributes to the identification of prevalent data acquired by sensors and points out the most popular processing techniques of aerial imagery. The obtained results revealed that UAVs are used at mining sites for safety purposes including mine surface displacements, stability monitoring, working environment, coal fire identification, drill and blast, and mine waste dump operations. This research hopes to provide a technical reference, expanding the knowledge and recognition of UAV monitoring to manage mine safety, as well as an assessment of applications in mining activities.

Single-Valued Neutrosophic MCDM Approaches Integrated with MEREC and RAM for the Selection of UAVs in Forest Fire Detection and Management

In recent times, the world has experienced a rise in the frequency of forest fires. These fires cause severe economic damage and pose a significant threat to human lives. Therefore, it is essential to search for solutions that can help combat fires and detect them early. Once a fire reaches a certain level, it becomes challenging to control it. Various systems have been proposed to collect data and detect forest fires, such as satellites and other traditional methods. However, these solutions have been ineffective in terms of cost, coverage of large areas, accuracy, and the safety of human lives. To address these limitations, Unmanned Aerial Vehicles (UAVs) or drones have been used for detecting, combatting, and early warning of forest fires. UAVs are one of the modern technologies that have achieved great progress in monitoring natural disasters and have been widely used in monitoring, detecting, and predicting fires. They can fly without a human pilot on board, which makes them ideal for preserving human life. In addition, they are equipped with firefighting tools and various tools for remote sensing. This is to take high-quality photos or videos of the area to be detected. Different types of UAVs are used to fight fires, and here decision-makers face a problem in choosing between these types. Therefore, this research proposes a new MCDM model integrated with neutrosophic sets for selecting the optimal UAV to combat forest fires; therefore it helps in effectively detecting and fighting the fire. The proposed model integrates a Method based on Removal Effects of Criteria (MEREC) and Root Assessment Method (RAM) with the context of neutrosophic sets that effectively deal with ambiguity for selecting the optimal UAV which use in the detection and combat forest fires.

ON-BOARD CONTROL-MEASUREMENT SYSTEM FOR MICRO CONVERTIPLANE-TYPE UNMANNED AERIAL VEHICLES

The article describes the on-board control-measurement system designed to record the main flight parameters of a micro-convertiplane type unmanned aerial vehicle and the element base used for this purpose. The methodology of conducting tests in stationary mode through the on-board control-measurement system was developed and tests were carried out. The engine's diagnostic check and condition assessment has been performed using the on-board control-measuring system. The possibility of on-ground check of the micro unmanned aerial vehicles power elements before and after the flight, as well as the possibility of monitoring the flight events that may occur in the air during the flight has been shown. The data on the power consumption and temperature of each engine in the stationary mode, the direction of the aircraft axes and the velocity vector have been recorded by mean of the developed on-board control-measurement system, as well as time dependence diagrams of the corresponding parameters have been built in comparative order based on the obtained values. A methodology has been developed for the diagnostics of the engine operation of the micro-unmanned aerial vehicle before and during the flight. It has been shown that the temperature, power consumption and number of revolutions of the engines included in the power system of the aircraft, as well as the aircraft axes and velocity vector direction data is recorded during the flight, by mean of the on-board control-measurement system designed to collect data on the flight-technical parameters of the convertiplane-type micro-unmanned aerial vehicles.

Vision-Based UAV Detection and Localization to Indoor Positioning System.

In recent years, the technological landscape has undergone a profound metamorphosis catalyzed by the widespread integration of drones across diverse sectors. Essential to the drone manufacturing process is comprehensive testing, typically conducted in controlled laboratory settings to uphold safety and privacy standards. However, a formidable challenge emerges due to the inherent limitations of GPS signals within indoor environments, posing a threat to the accuracy of drone positioning. This limitation not only jeopardizes testing validity but also introduces instability and inaccuracies, compromising the assessment of drone performance. Given the pivotal role of precise GPS-derived data in drone autopilots, addressing this indoor-based GPS constraint is imperative to ensure the reliability and resilience of unmanned aerial vehicles (UAVs). This paper delves into the implementation of an Indoor Positioning System (IPS) leveraging computer vision. The proposed system endeavors to detect and localize UAVs within indoor environments through an enhanced vision-based triangulation approach. A comparative analysis with alternative positioning methodologies is undertaken to ascertain the efficacy of the proposed system. The results obtained showcase the efficiency and precision of the designed system in detecting and localizing various types of UAVs, underscoring its potential to advance the field of indoor drone navigation and testing.

Location and routing of armed Unmanned Aerial Vehicles and carrier platforms against mobile targets

In this study, we consider a real-life combinatorial optimization problem related to deploying and routing Unmanned Aerial Vehicles (UAVs) and naval carrier platforms. In particular, we seek to determine the initial locations for carrier platforms and the optimal type and number of UAVs to be stationed on each carrier platform as well as their spatial/temporal routes for engaging hostile surface targets in the region. Our modeling framework incorporates a number of realistic but challenging ingredients and assumptions such as the mobility of surface targets and carrier platforms during the mission, capacitated multiple platforms and UAVs, UAV-carrier platform compatibility, and allowance for different takeoff/land on platforms for UAVs. In the effort to represent the problem mathematically, we first formulated an Integer Linear Program (ILP) model which seeks to maximize the total time-dependent weights of the targets engaged. Next, we proposed a heuristic solution algorithm based on the ant colony optimization framework. Our computational experiments performed on instances with different sizes showed that the heuristic approach achieves high-quality solutions even for large-size problem instances in short CPU times.

Effect of interface design on cognitive workload in unmanned aerial vehicle control

Unmanned Aerial Vehicle (UAV) control interfaces are critical channels for transferring information between the vehicle and an operator. Research on system performance has focused on enhancing vehicle automation and some work has evaluated cognitive workload for existing UAV interfaces. The potential for usable interface design to reduce cognitive workload during the early design phase has been largely overlooked. This study addresses these gaps by: (1) evaluating the effectiveness of a contemporary UAV interface design tool (the Modified GEDIS-UAV) to moderate user workload; (2) examining the effectiveness of various UAV interface designs for minimizing cognitive workload under different control task pacing; and (3) exploring the use of eye tracking measures, traditionally applied in other domains, as indicators of cognitive workload in UAV operations. We prototyped three different interface designs, classified as “baseline”, “enhanced” and “degraded” interfaces. Cognitive workload in UAV operation was manipulated in terms of levels of vehicle speed (“low” and “high”). Physiological and subjective measures of workload were collected for all combinations of interface design and task demand. Results revealed the “enhanced” interface to yield the lowest operator cognitive workload and supported operator resilience to increased control task demand, as compared to the “baseline” and “degraded” interfaces. In addition, task demand was found to elevate operator cognitive workload, particularly in terms of "mental" and "temporal" demands and operator perceptions of "performance". The study also demonstrated utility of eye-tracking technology for detecting cognitive workload in UAV operations. This research provides practical guidance for UAV control interface design to manage operator workload. The methods employed in the study are applicable to interface evaluation for various types of UAVs and other unmanned systems to enhance human-automation interaction.

A Robust Decision-Making Model for Medical Supplies via Selecting Appropriate Unmanned Aerial Vehicle

Recently, Unmanned Aerial Vehicles (UAVs) have been used in many fields, including the field of health care, especially in delivering the necessary medical equipment and supplies, due to the many advantages they have compared to other traditional methods and the presence of different types of UAVs, to improve healthcare and provide it with the medical supplies and equipment necessary to save the lives of patients. Choosing the appropriate UAV for a specific situation represents a problem facing decision-makers, which is considered a multi-criteria decision-making problem. Since the decision-making process is cumbersome and complex, and deals with uncertainty and ambiguity. In this research, we proposed multi-criteria decision-making (MCDM) model using CRITERIA (Criteria Importance through Intercriteria Correlation) and MARICA (Multi-Attribute Rating Analysis with Ideal Concepts) methods integrated with neutrosophic logic, which is considered a powerful tool in dealing with uncertainty and ambiguity. The CRITIC method calculates the weight of criteria, whereas it takes into account the correlations and relationships between the criteria, whether they are positive or negative, unlike other methods that consider the criteria separately, which allows for a more accurate and comprehensive analysis of the decision problem. The MARICA method is used also to rank the alternatives. It allows decision-makers to evaluate alternatives according to how well they perform across multiple criteria by considering several factors at once. This helps increase the effectiveness of judgments by taking into account all relevant factors. Moreover, MARICA is a user-friendly method that doesn't require complex mathematical calculations, making it accessible to anyone who wants to make sound choices. The UAV with the highest ranking is the one that will be chosen and represents the best among the alternatives. The proposed model proved its effectiveness by applying it to an experimental case.

Unmanned aerial vehicle as an air defense target

There have been reports of unauthorized use of unmanned aerial vehicles (UAVs) in highly controlled areas (airports, military installations, against critical industrial infrastructure) in the media since the mid-2000s. Nowadays, small UAVs are widely used for unauthorized surveillance of important objects, conducting terrorist attacks and sabotage, carrying prohibited goods (weapons, drugs), as well as for military purposes. For this reason, the problem of countering UAVs, and especially small UAVs, has become extremely relevant. Analysis of publications in this area has shown a small number of serious studies on this topic. There are often too optimistic conclusions about the effectiveness of existing air defense systems for countering all types of UAVs in many papers. However, the problem of countering UAVs, and especially small UAVs, is highly complex, multi-faceted, and has not been solved yet. The purpose of this paper is to systematize and analyze various ways and means of countering UAVs, as well as to form general directions for effective solution of the problem. The material presented in the paper focuses on the analysis of the capabilities of air defense means to physical countering UAVs. The results of systematization and analysis of various methods and means of countering UAVs, which are based on physical interception, are presented in the article. This systematization is based on more than 60 open sources. The analysis of the sources show the main features of the UAV as an object of destruction and made possible a detailed multi-aspect analysis of modern air defense systems, their effectiveness and disadvantage. Suggestions for improving the effectiveness of the air defense systems what are used against UAVs are also summarized in this paper. Elements of the novelty of the paper are the general features of the detection and destruction of UAVs as well as systemic disadvantage in technological solutions of the air defense systems which lead to a decrease in their combat effectiveness when they are used against UAVs. The material of the paper can be used to generate initial data for modeling and studying the combat effectiveness of the air defense systems when countering UAVs. This article can be useful for designers who design countering UAV systems as well.

Unmanned aerial vehicle as a reconnaissance object

There have been reports of unauthorized use of unmanned aerial vehicles (UAVs) in highly controlled areas (airports, military facilities, against critical industrial infrastructure) in the media since the mid-2000s. Nowadays, small UAVs are widely used for unauthorized surveillance of important objects, conducting terrorist attacks and sabotage, transporting prohibited goods (weapons, drugs), as well as for military purposes. For this reason, the problem of countering UAVs, and especially small UAVs, has be-come extremely relevant. Analysis of publications in this area has revealed a small number of serious studies in this topic. Conclusions, made on the effectiveness of existing air defense systems for combating all types of UAVs in several papers seem to be too optimistic. However, the problem of countering UAVs, and especially small UAVs, is highly complicated, multifaceted and has not been solved yet. The goal of this paper is to analyze UAV as an object of detection and defeating while using various ways and means of countering UAVs. This work focuses on the analysis of UAV as an object of radar, radio-reconnaissance and radio-technical, optical-electronic and acoustic intelligence, as well as an object of fire and electronic defeating. Results of systematization and analysis of UAVs as an object of fire and electronic defeating, capabilities of on time detecting and target indicating by radar, radio-reconnaissance and radio-technical, optical-electronic and acoustic means of intelligence are presented in the paper. Carried out systematization is based on information from more than 40 open sources. Analysis of the sources reveals the list of main features of the UAV as an object of defeating, and makes it possible to carry out a detailed analysis of modern detection systems, as well as their effectiveness and disadvantages. Elements of novelty of the paper are general features of UAVs detection process, as well as systemic disadvantages of the detection systems technical solutions, which lead to reduce in efficiency, while being used against UAVs. The material of the paper can be used to generate initial data for modeling and studying the combat effectiveness of the air defense systems when countering UAVs. This article can be useful for constructors, who design the countering UAV systems.

MVT: Multi-Vision Transformer for Event-Based Small Target Detection

MVT: Multi-Vision Transformer for Event-Based Small Target Detection